Surveys of Various Computer Vision Topics
Surveys of particular topics are scattered throughout the Computer Vision Bibliography under the appropriate topics and near the other related papers. Many of the topics in the Bibliography have a subsection devoted to general or survey articles so a good starting point for surveys is the contents page for the appropriate major topic. Current research and applications are also highlighted in various general and topical Computer Vision and Image Processing conferences. The more limited, topic specific, workshops can also provide a good introduction to the current status of some topic.
The bibliography links to available survey articles have been extracted to simplify finding them. Many of the surveys may be available online as shown by a link in the entry itself.
Computer Vision resources include:
- Computer Vision Conference Listing
- Code for Computer Vision Algorithms
- Available Datasets
- Computer Vision Topical Surveys
- Vendors. These may also have related research groups.
- Research Groups
- Researchers within Research Groups and Researchers with unlisted groups
- Browse Papers by Author Name
- Professional Societies
For more information on the topics, contact information, etc. see the annotated Computer Vision Bibliography or the Complete Conference Listing for Computer Vision and Image Analysis
Detailed Entries for Survey
Swain, M.J., Stricker, M.A.,Promising Directions in Active Vision,
IJCV(11), No. 2, October 1993, pp. 109-126.
Springer DOI Survey, Active Vision. Introductory paper in special issue on apctive vision. review/survey.
Aloimonos, Y.[Yiannis],
Purposive, Qualitative, Active Vision,
CVGIP(56), No. 1, July 1992, pp. 1-2.
Elsevier DOI
And:
Purposive and Qualitative Active Vision,
DARPA90(816-828).
And: ICPR90(I: 346-360).
IEEE DOI Survey, Active Vision. Active Vision, Survey. The Journal article is only an introduction to the special issue. Long survey paper.
Ballard, D.H.[Dana H.],
Animate Vision,
AI(48), No. 1, February 1991, pp. 57-86.
Elsevier DOI Survey, Active Vision. Active Vision, Survey.
Ballard, D.H.[Dana H.], Brown, C.M.[Christopher M.],
Principles of Animate Vision,
CVGIP(56), No. 1, July 1992, pp. 3-21.
Elsevier DOI
And: ActPercep93 Survey, Active Vision. Active Vision, Survey.
Nguyen, T.V.[Tam V.], Zhao, Q.[Qi], Yan, S.C.[Shui-Cheng],
Attentive Systems: A Survey,
IJCV(126), No. 1, January 2018, pp. 86-110.
Springer DOI
Survey, Attention.
Kimura, A.[Akisato], Yonetani, R.[Ryo], Hirayama, T.[Takatsugu],
Computational Models of Human Visual Attention and Their Implementations: A Survey,
IEICE(E96-D), No. 3, March 2013, pp. 562-578.
WWW Link.
Survey, Attention.
Tarabanis, K.A., Allen, P.K., Tsai, R.Y.,
A Survey of Sensor Planning in Computer Vision,
RA(11), No. 1, February 1995, pp. 86-104. Survey, Sensor Planning.
Scott, W.R.[William R.], Roth, G.[Gerhard], Rivest, J.F.[Jean-François],
View planning for automated three-dimensional object reconstruction and inspection,
Surveys(35), No. 1, March 2003, pp. 64-96.
WWW Link.
Survey, View Planning.
Earlier:
View planning with a registration constraint,
3DIM01(127-134).
IEEE DOI
Guo, H.H.[Hui-Hui], Wu, F.[Fan], Qin, Y.[Yunchuan], Li, R.[Ruihui], Li, K.Q.[Ke-Qin], Li, K.[Kenli],
Recent Trends in Task and Motion Planning for Robotics: A Survey,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link
Survey, Motion Planning. Task and motion planning, online planning, learning for planning
Roy, S.D.[Sumantra Dutta], Chaudhury, S.[Santanu], Banerjee, S.[Subhashis],
Active recognition through next view planning: a survey,
PR(37), No. 3, March 2004, pp. 429-446.
Elsevier DOI
Survey, Sensor Planning.
Hwang, Y.K., Ahuja, N.,
Gross Motion Planning: A Survey,
Surveys(24), No. 3, September 1992, pp. 219-291. Survey, Motion Planning.
CVonline: System Models, Calibration and Parameter Estimation,
CV-OnlineJuly 2001.
HTML Version. Survey, Calibration.
Zhang, Z.Y.[Zheng-You],
Camera Calibration,
ETCV04(Chapter 2). Survey, Camera Calibration.
CVOnline: Autocalibration,
CV-Online2006.
WWW Link. Survey, Autocalibration.
Hemayed, E.E.,
A survey of camera self-calibration,
AVSBS03(351-357).
IEEE DOI
Survey, Self Calibration.
Salvi, J., Armangué, X., Pagés, J.,
A Survey Addressing the Fundamental Matrix Estimation Problem,
ICIP01(II: 209-212).
IEEE DOI
Survey, Fundamental Matrix.
Ayaz, S.M.[Shirazi Muhammad], Kim, M.Y.[Min Young], Park, J.[Jaechan],
Survey on zoom-lens calibration methods and techniques,
MVA(28), No. 8, November 2017, pp. 803-818.
WWW Link.
Survey, Calibration.
Thrun, S.[Sebastian],
Toward Robotic Cars,
CACM(53), No. 4, April 2010, pp. 99-106.
DOI Link
Survey, Autonomous Vehicles. Recent challenges organized by DARPA have induced a significant advance in technology for autopilots for cars; similar to those already used in aircraft and marine vessels. This article reviews this technology.
Schneiderman, R.,
Unmanned Drones are Flying High in the Military/Aerospace Sector,
SPMag(29), No. 1, 2012, pp. 8-11.
IEEE DOI
Survey, UAV. Special Report
Section, Multiple Entries: 15.3.1.7 Autonomous Vehicles, Surveys, Collections, Overviews Chapter Contents (Back)
Survey, Autonomous Vehicles. Autonomous Vehicles.
See also Autonomous Vehicle Safety, Evaluation, Analysis.
Broggi, A.[Alberto], Bertozzi, M.[Massimo], Conte, G.[Gianni], Fascioli, A.[Alessandra],
Automatic Vehicle Guidance: The Experinces of the ARGO Autonomous Vehicle,
World Scientific1999, ISBN 981-02-3720-0. Survey, Vehicle Guidance. Surveys work in guidance up through the ARGO project. The ARGO vehicle drove 2000km over the Italian highway network.
de Souza, G.N.[Guilherme N.], Kak, A.C.[Avinash C.],
Vision for Mobile Robot Navigation: A Survey,
PAMI(24), No. 2, February 2002, pp. 237-267.
IEEE DOI
Mobile Robots. Survey, Mobile Robots. Reviews 20 years of work.
Brooks, R.,
Robotic cars won't understand us, and we won't cut them much slack,
Spectrum(54), No. 8, August 2017, pp. 34-51.
IEEE DOI
Survey, Autonomous Vehicles. Automobiles, Autonomous automobiles, Legged locomotion, Roads, Urban areas
Yasuda, Y.D.V.[Yuri D. V.], Martins, L.E.G.[Luiz Eduardo G.], Cappabianco, F.A.M.[Fabio A. M.],
Autonomous Visual Navigation for Mobile Robots: A Systematic Literature Review,
Surveys(53), No. 1, February 2020, pp. xx-yy.
DOI Link
Survey, Autonomous Navigation. Mobile robots, visual navigation, systematic literature review, autonomous navigation
Janai, J.[Joel], Güney, F.[Fatma], Behl, A.[Aseem], Geiger, A.[Andreas],
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art,
FTCGV(12), No. 1-3, 2020, pp. 1-308.
DOI Link
Survey, Autonomous Vehicles.
Qian, R.[Rui], Lai, X.[Xin], Li, X.R.[Xi-Rong],
3D Object Detection for Autonomous Driving: A Survey,
PR(130), 2022, pp. 108796.
Elsevier DOI
Survey, Object Detection. 3D object detection, Autonomous driving, Point clouds
Mao, J.G.[Jia-Geng], Shi, S.S.[Shao-Shuai], Wang, X.G.[Xiao-Gang], Li, H.S.[Hong-Sheng],
3D Object Detection for Autonomous Driving: A Comprehensive Survey,
IJCV(131), No. 8, August 2023, pp. 1909-1963.
Springer DOI
Survey, Object Detection.
Bila, C., Sivrikaya, F., Khan, M.A., Albayrak, S.,
Vehicles of the Future: A Survey of Research on Safety Issues,
ITS(18), No. 5, May 2017, pp. 1046-1065.
IEEE DOI
Survey, Driver Assistance. Collision avoidance, Roads, Safety, Stereo vision, Taxonomy, Vehicles, Advanced driver assistance systems, collision avoidance, connected vehicles, intelligent vehicles, vehicle detection, vehicle, safety
Casner, S.M.[Stephen M.], Hutchins, E.L.[Edwin L.], Norman, D.[Don],
The Challenges of Partially Automated Driving,
CACM(59), No. 5, May 2016, pp. 70-77.
DOI Link
Survey, Driver Assistance. Issues of driver assistance.
Martinez, C.M.[C. Marina], Heucke, M., Wang, F.Y., Gao, B., Cao, D.,
Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey,
ITS(19), No. 3, March 2018, pp. 666-676.
IEEE DOI
Survey, Driver Assistance. Acceleration, Automation, Fuels, Intelligent vehicles, Roads, Safety, Driving style, driver behavior, driving conditions, driving safety, machine learning
Dickmanns, E.D.[Ernst D.],
Dynamic Vision,
Online2011.
WWW Link. Bibliography, Motion. Survey, Motion. Survey, Dynamic Vision.
References to early work in dynamic vision. Don't forget that many problems were solved years ago. Primarily the subset dealing with vehicles, vehicle control, vision from moving vehicles.
Dickmanns, E.D.[Ernst D.],
Dynamic Vision for Perception and Control of Motion,
Springer2007, ISBN 978-1-84628-637-7.
WWW Link. Survey, Autonomous Vehicles. Overview of the 20 years of research in dynamic scene understanding.
Dickmanns, E.D.[Ernst D.],
Vehicles capable of dynamic vision: A new breed of technical beings?,
AI(103), No. 1-2, August 1998, pp. 49-76.
Elsevier DOI
Earlier:
Vehicles Capable of Dynamic Vision,
IJCAI97(1577-1592). Survey, Autonomous Vehicles.
Dakopoulos, D., Bourbakis, N.G.,
Wearable Obstacle Avoidance Electronic Travel Aids for Blind: A Survey,
SMC-C(40), No. 1, January 2010, pp. 25-35.
IEEE DOI
Survey, Assistance for Blind.
Jafri, R.[Rabia], Ali, S.A.[Syed Abid], Arabnia, H.R.[Hamid R.], Fatima, S.[Shameem],
Computer vision-based object recognition for the visually impaired in an indoors environment: a survey,
VC(30), No. 11, November 2014, pp. 1197-1222.
WWW Link.
Survey, Visual Assistance.
McCall, J.C., Trivedi, M.M.,
Video-based lane estimation and tracking for driver assistance: Survey, system, and evaluation,
ITS(7), No. 1, March 2006, pp. 20-37.
IEEE DOI
Survey, Driver Assistance.
Yenikaya, S.[Sibel], Yenikaya, G.[Gökhan], Düven, E.[Ekrem],
Keeping the vehicle on the road: A survey on on-road lane detection systems,
Surveys(46), No. 1, October 2013, pp. Article No 2.
DOI Link
Survey, Lane Detection. The development of wireless sensor networks, such as researchers Advanced Driver Assistance Systems (ADAS) requires the ability to analyze the road scene just like a human does.
Manoharan, K.[Kodeeswari], Daniel, P.[Philemon],
Survey on various lane and driver detection techniques based on image processing for hilly terrain,
IET-IPR(12), No. 9, September 2018, pp. 1511-1520.
DOI Link
Survey, Lane Detection. Survey, Driver Monitoring.
Piasco, N.[Nathan], Sidibé, D.[Désiré], Demonceaux, C.[Cédric], Gouet-Brunet, V.[Valérie],
A survey on Visual-Based Localization: On the benefit of heterogeneous data,
PR(74), No. 1, 2018, pp. 90-109.
Elsevier DOI
Survey, Localization. Image-based localization
Tariq, Z.B.[Zain Bin], Cheema, D.M.[Dost Muhammad], Kamran, M.Z.[Muhammad Zahir], Naqvi, I.H.[Ijaz Haider],
Non-GPS Positioning Systems: A Survey,
Surveys(50), No. 4, November 2017, pp. Article No 57.
DOI Link
Survey, Localization.
Schmidt, D.[Desmond], Radke, K.[Kenneth], Camtepe, S.[Seyit], Foo, E.[Ernest], Ren, M.[Michal],
A Survey and Analysis of the GNSS Spoofing Threat and Countermeasures,
Surveys(48), No. 4, May 2016, pp. Article No 64.
DOI Link
Survey, GNSS. Assess in detail the exact nature of threat scenarios posed by spoofing against the most commonly cited targets; second, to investigate the many practical impediments, often underplayed, to carrying out GNSS spoofing attacks in the field; and third, to survey and assess the effectiveness of a wide range of proposed defences against GNSS spoofing.
Maghdid, H.S.[Halgurd S.], Lami, I.A.[Ihsan Alshahib], Ghafoor, K.Z.[Kayhan Zrar], Lloret, J.[Jaime],
Seamless Outdoors-Indoors Localization Solutions on Smartphones: Implementation and Challenges,
Surveys(48), No. 4, May 2016, pp. Article No 53.
DOI Link
Survey, Localization. This article reviews the recent research-oriented and commercial localization solutions on smartphones. The focus of this article is on the implementation challenges associated with utilizing these positioning solutions on Android-based smartphones.
Yap, K.H.[Kim-Hui], Chen, T.[Tao], Li, Z.[Zhen], Wu, K.[Kui],
A Comparative Study of Mobile-Based Landmark Recognition Techniques,
IEEE_Int_Sys(25), No. 1, January-February 2010, pp. 48-57.
IEEE DOI
Survey, Landmark Recognition. Mobile phone based. Where is this picture taken.
Bar Hillel, A.[Aharon], Lerner, R.[Ronen], Levi, D.[Dan], Raz, G.[Guy],
Recent progress in road and lane detection: a survey,
MVA(25), No. 3, April 2014, pp. 727-745.
WWW Link.
Survey, Road Detection.
Rateke, T.[Thiago], Justen, K.A.[Karla A.], Chiarella, V.F.[Vito F.], Sobieranski, A.C.[Antonio C.], Comunello, E.[Eros], von Wangenheim, A.[Aldo],
Passive Vision Region-Based Road Detection: A Literature Review,
Surveys(51), No. 1, February 2019, pp. Article No 31.
DOI Link
Survey, Road Detection. road detection based upon frontal images
Mukhtar, A., Xia, L.[Likun], Tang, T.B.[Tong Boon],
Vehicle Detection Techniques for Collision Avoidance Systems: A Review,
ITS(16), No. 5, October 2015, pp. 2318-2338.
IEEE DOI
Survey, Collicion Avoidance. collision avoidance
Gandhi, T.[Tarak], Trivedi, M.M.,
Vehicle Surround Capture: Survey of Techniques and a Novel Omni-Video-Based Approach for Dynamic Panoramic Surround Maps,
ITS(7), No. 3, September 2006, pp. 293-308.
IEEE DOI
Survey, Driver Assistance.
Ghahremannezhad, H.[Hadi], Shi, H.[Hang], Liu, C.J.[Cheng-Jun],
Object Detection in Traffic Videos: A Survey,
ITS(24), No. 7, July 2023, pp. 6780-6799.
IEEE DOI
Survey, Object Detection. Object detection, Feature extraction, Surveillance, Traffic control, Videos, Cameras, Motion segmentation, traffic surveillance
Saadna, Y.[Yassmina], Behloul, A.[Ali],
An overview of traffic sign detection and classification methods,
MultInfoRetr(6), No. 3, September 2017, pp. 193-210.
Springer DOI
Survey, Traffic Signs.
Wilson, A.[Andrew],
Riding the Rails,
VisSys(16), No. 1, January 2011. Survey, Rail Inspection. Railway tunnels, bridges, and underpasses can be imaged at high speeds using linescan and area-array cameras.
Lawton, D.T., and McConnell, C.C.,
Image Understanding Environments,
PIEEE(76), No. 8, August 1988, pp. 1036-1050. Survey, Systems. Systems, Survey. General discussion of IU Environments in terms of components, representations, programming constructs, data bases, and interfaces with examples (mostly from ADS systems).
CVonline: Visual Processing Software and Environments,
CV-OnlineJuly 2001.
HTML Version. Survey, Systems.
See also University of Edinburgh.
CVonline: Hardware, DSP, Parallel and Other Non-Standard Processing Platforms,
CV-OnlineJuly 2001.
HTML Version. Survey, Systems.
See also University of Edinburgh.
Section, Multiple Entries: 20.2.2 Parallel and Multi-Processor Algorithms, General, Survey Chapter Contents (Back)
Survey, Systems. Survey, Architectures. Architectures. Parallel Algorithms.
Reeves, A.P.[Anthony P.],
Parallel Computer Architectures for Image Processing,
CVGIP(25), No. 1, January 1984, pp. 68-88.
Elsevier DOI Survey, Systems. Survey, Architectures. Architectures, Survey. A survey article about various parallel methods and what they can do for image processing algorithms.
Lee, G.G.C.[Gwo Giun C.], Chen, Y.K.[Yen-Kuang], Mattavelli, M., Jang, E.S.,
Algorithm/Architecture Co-Exploration of Visual Computing on Emergent Platforms: Overview and Future Prospects,
CirSysVideo(19), No. 11, November 2009, pp. 1576-1587.
IEEE DOI
Survey, Parallel Processing. Survey, Hardware.
Lim, H.S., and Binford, T.O.,
Survey of Parallel Computers,
DARPA87(644-654).
Earlier:
Survey of Array Processors,
DARPA84(334-343). Survey, Systems. Architectures, Survey. Survey of the available (parallel) array processors, their costs, capabilities and potential uses.
Hawkes, P.W.,
Electron Image Processing: A Survey,
CGIP(8), No. 3, December 1978, pp. 406-442.
Elsevier DOI Survey, Inspection.
Ribeiro, E.[Eraldo], Shah, M.[Mubarak],
Computer Vision for Nanoscale Imaging,
MVA(17), No. 3, August 2006, pp. 147-162.
Springer DOI
Survey, Nanoscale Imaging.
Section, Multiple Entries: 20.4.1 Image Database, Retrieval -- Surveys, Evaluations Chapter Contents (Back)
Survey, Image Retrieval. Database. Image Database. CBIR.
See also Object Recognition, Retrieval Datasets.
CVonline: Databases and Indexing,
CV-OnlineJuly 2001.
HTML Version. Survey, Indexing.
See also University of Edinburgh.
Tamura, H.[Hideyuki], Yokoya, N.[Naokazu],
Image Database Systems: A Survey,
PR(17), No. 1, 1984, pp. 29-43.
Elsevier DOI Survey, Image Retrieval.
Aigrain, P., Zhang, H.J., Petkovic, D.,
Content-Based Representation and Retrieval of Visual Media: A State-of-the-Art Review,
MultToolApp(3), No. 3, November 1996, pp. 179-202.
Survey, Image Retrieval.
de Marsico, M., Cinque, L., Levialdi, S.,
Indexing Pictorial Documents by Their Content: A Survey of Current Techniques,
IVC(15), No. 2, February 1997, pp. 119-141.
Elsevier DOI
Survey, Image Retrieval.
Catarci, T., Costabile, M.F., Levialdi, S., Batini, C.,
Visual Query Systems for Databases: A Survey,
JVLC(8), No. 2, April 1997, pp. 215-260.
Survey, Image Retrieval.
Abraham, T.[Tamas], Roddick, J.F.[John F.],
Survey of Spatio-Temporal Databases,
GeoInfo(3), No. 1, March 1999, pp. 61-99.
DOI Link Survey, GIS.
Mandal, M.K., Idris, F.M., Panchanathan, S.,
A critical evaluation of image and video indexing techniques in the compressed domain,
IVC(17), No. 7, May 1999, pp. 513-529.
Elsevier DOI Survey, Image Retrieval.
Smeulders, A.W.M.[Arnold W.M.], Jain, R.C.[Ramesh C.],
Image Databases and Multi-Media Search,
World Scientific1998, ISBN 981-02-3327-2. Survey, Image Retrieval. Survey of the field. The proceedings of an earlier workshop.
Smeulders, A.W.M.[Arnold W.M.], Worring, M.[Marcel], Santini, S.[Simone], Gupta, A.[Amarnath], Jain, R.C.[Ramesh C.],
Content-Based Image Retrieval at the End of the Early Years,
PAMI(22), No. 12, December 2000, pp. 1349-1380.
IEEE DOI
Survey, Image Retrieval. With 200 references. A detailed survey of the early years of CBIR.
Lew, M.S.[Michael S.],
Principles of Visual Information Retrieval,
Springer-Verlag2001. ISBN 1-85233-381-2. Survey, Image Retrieval. Fundamentals: color, texture, shape, features, video. Advanced topics: query languages, feedback, integrating, trademark retrieval. Buy this book: Principles Of Visual Information Retrieval (ADVANCES IN PATTERN RECOGNITION)
Antani, S.[Sameer], Kasturi, R.[Rangachar], Jain, R.[Ramesh],
A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video,
PR(35), No. 4, April 2002, pp. 945-965.
Elsevier DOI
Survey, Image Retrieval.
Veltkamp, R.C.[Remco C.], Burkhardt, H.[Hans], Kriegel, H.P.[Hans-Peter],
State-of-the-Art in Content-Based Image and Video Retrieval,
KluwerOctober 2001, ISBN 1-4020-0109-6
WWW Link. Survey, Image Retrieval. Buy this book: State-of-the-art In Content-based Image And Video Retrieval (COMPUTATIONAL IMAGING AND VISION)
Gevers, T.[Theo], Smeulders, A.W.M.[Arnold W.M.],
Content-Based Image Retrieval: An Overview,
ETCV04(Chapter 8). Survey, Image Retrieval.
Liu, Y.[Ying], Zhang, D.S.[Deng-Sheng], Lu, G.J.[Guo-Jun], Ma, W.Y.[Wei-Ying],
A survey of content-based image retrieval with high-level semantics,
PR(40), No. 1, January 2007, pp. 262-282.
Elsevier DOI
Award, Pattern Recognition. Survey, Image Retrieval. Content-based image retrieval; Semantic gap; High-level semantics
Zhang, D.S.[Deng-Sheng], Islam, M.M.[Md. Monirul], Lu, G.J.[Guo-Jun],
A review on automatic image annotation techniques,
PR(45), No. 1, 2012, pp. 346-362.
Elsevier DOI
Award, Pattern Recognition, Honorable Mention. Survey, Image Annotation. Image retrieval
Yang, Y., Lin, H., Zhang, Y.,
Content-Based 3-D Model Retrieval: A Survey,
SMC-C(37), No. 6, November 2007, pp. 1081-1098.
IEEE DOI
Survey, Image Retrieval.
Vasconcelos, N.M.[Nuno M.],
From Pixels to Semantic Spaces: Advances in Content-Based Image Retrieval,
Computer(40), No. 7, July 2007, pp. 20-26.
IEEE DOI
Survey, Image Retrieval.
Earlier:
On the Complexity of Probabilistic Image Retrieval,
ICCV01(II: 400-407).
IEEE DOI
Datta, R.[Ritendra], Joshi, D.[Dhiraj], Li, J.[Jia], Wang, J.Z.[James Z.],
Image retrieval: Ideas, influences, and trends of the new age,
Surveys(40), No. 2, April 2008, pp. 1-60.
WWW Link.
Survey, Image Retrieval.
And:
Studying Aesthetics in Photographic Images Using a Computational Approach,
ECCV06(III: 288-301).
Springer DOI
Extract measures corresponding to aesthetic rules.
Lew, M.S.[Michael S.], Sebe, N.[Nicu], Djerba, C., Jain, R.,
Content-Based Multimedia Information Retrieval: State-of-the-Art and Challenges,
TMM(2), No. 1, 2006, pp. 1-19. Survey, Image Retrieval.
Tsai, C.F.[Chih-Fong], Hung, C.L.[Chih-Li],
Automatically Annotating Images with Keywords: A Review of Image Annotation Systems,
RPCS(1), No. 1, January 2008, pp. 55-68.
WWW Link. Survey, Image Annotation.
Müller, H., Clough, P., Deselaers, T., Caputo, B., (Eds.)
ImageCLEF: Experimental Evaluation in Visual Information Retrieval,
Springer2010, ISBN: 978-3-642-15180-4
WWW Link. Survey, Retrieval. Buy this book: ImageCLEF: Experimental Evaluation in Visual Information Retrieval (The Information Retrieval Series)
Alzu'bi, A.[Ahmad], Amira, A.[Abbes], Ramzan, N.[Naeem],
Semantic content-based image retrieval: A comprehensive study,
JVCIR(32), No. 1, 2015, pp. 20-54.
Elsevier DOI
Survey, CBIR. CBIR
Zheng, L.[Liang], Yang, Y.[Yi], Tian, Q.[Qi],
SIFT Meets CNN: A Decade Survey of Instance Retrieval,
PAMI(40), No. 5, May 2018, pp. 1224-1244.
IEEE DOI
Survey, CBIR. Computational modeling, Detectors, Encoding, Feature extraction, Image retrieval, Indexes, Visualization, Instance retrieval, SIFT, literature survey
Johansson, B.[Björn],
A Survey on: Contents Based Search in Image Databases,
TRLiTH-ISY-R-2215, Linkoping University, CVL, February, 2000.
PDF File. Survey, Image Retrieval.
Grosky, W.I., and Mehrotra, R.,
Image Database Management,
Computer(22), No. 12, December 1989. Survey, Database. Database, Survey. Special issue. Some database papers mixed with general vision system papers.
Li, Y.[Yi], Li, W.Z.[Wen-Zhao],
A survey of sketch-based image retrieval,
MVA(29), No. 7, October 2018, pp. 1083-1100.
WWW Link.
Survey, Sketch-Based Retrieval.
Zhao, Y.[Yang], Ren, D.Y.[Di-Ya], Jia, W.[Wei], Wang, R.G.[Rong-Gang], Liu, X.P.[Xiao-Ping],
Cartoon Image Processing: A Survey,
IJCV(130), No. 11, November 2022, pp. 2733-2769.
Springer DOI
Survey, Cartoons.
Kherfi, M.L., Ziou, D., Bernardi, A.,
Image Retrieval from the World Wide Web: Issues, Techniques, and Systems,
Surveys(36), No. 1, March 2004, pp. 35-67.
WWW Link.
Survey, Image Retrieval.
Thomee, B.[Bart], Lew, M.S.[Michael S.],
Interactive search in image retrieval: a survey,
MultInfoRetr(1), No. 2, July 2012, pp. 71-86.
WWW Link.
Survey, Image Retrieval.
Das, P.[Pranjit], Neelima, A.[Arambam],
An overview of approaches for content-based medical image retrieval,
MultInfoRetr(6), No. 4, December 2017, pp. 271-280.
Springer DOI
Survey, Medical CBIR.
Mei, T.[Tao], Rui, Y.[Yong], Li, S.P.[Shi-Peng], Tian, Q.[Qi],
Multimedia search reranking: A literature survey,
Surveys(46), No. 3, February 2014, pp. Article No 38.
DOI Link
Survey, Reranking. The explosive growth and widespread accessibility of community-contributed media content on the Internet have led to a surge of research activity in multimedia search. Approaches that apply text search techniques for multimedia search have achieved limited success.
Wu, Q.[Qi], Teney, D.[Damien], Wang, P.[Peng], Shen, C.H.[Chun-Hua], Dick, A.[Anthony], van den Hengel, A.J.[Anton J.],
Visual question answering: A survey of methods and datasets,
CVIU(163), No. 1, 2017, pp. 21-40.
Elsevier DOI
Survey, Visual Question Answering. Visual question answering
Teney, D.[Damien], Wu, Q., van den Hengel, A.J.[Anton J.],
Visual Question Answering: A Tutorial,
SPMag(34), No. 6, November 2017, pp. 63-75.
IEEE DOI
Survey, Visual Question Answering. Bioinformatics, Genomics, Machine learning, Visualization
Schoeffmann, K.[Klaus], Hudelist, M.A.[Marco A.], Huber, J.[Jochen],
Video Interaction Tools: A Survey of Recent Work,
Surveys(48), No. 1, September 2015, pp. 14:1-14:34.
DOI Link
Survey, Video Interaction. Video search and retrieval, human-computer interaction, mobile devices
Huang, Y.Z.[Yong-Zhen], Wu, Z.[Zifeng], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Feature Coding in Image Classification: A Comprehensive Study,
PAMI(36), No. 3, March 2014, pp. 493-506.
IEEE DOI
Survey, Feature Coding. feature extraction
Tousch, A.M.[Anne-Marie], Herbin, S.[Stéphane], Audibert, J.Y.[Jean-Yves],
Semantic hierarchies for image annotation: A survey,
PR(45), No. 1, 2012, pp. 333-345.
Elsevier DOI
Survey, Annotation. Image annotation
Bustos, B.[Benjamin], Keim, D.A.[Daniel A.], Saupe, D.[Dietmar], Schreck, T.[Tobias], Vranic, D.V.[Dejan V.],
Feature-based similarity search in 3D object databases,
Surveys(37), No. 4, December 2005, pp. 345-387.
WWW Link.
Survey, Image Retrieval.
Earlier:
An experimental comparison of feature-based 3D retrieval methods,
3DPVT04(215-222).
IEEE DOI
Section, Multiple Entries: 20.4.5.1 Video Database General, Overview, Survey and Evaluations Chapter Contents (Back)
Survey, Image Retrieval. Survey, Image Database. Database. Evaluation, Indexing. Image Database. Video Indexing.
Cowan, C., Cen, S.W., Walpole, J., Pu, C.,
Adaptive Methods for Distributed Video Presentation,
Surveys(27), No. 4, December 1995, pp. 580-583. Survey, Image Retrieval.
Dimitrova, N.,
The Myth of Semantic Video Retrieval,
Surveys(27), No. 4, December 1995, pp. 584-586. Survey, Image Retrieval.
Brezeale, D.[Darin], Cook, D.J.[Diane J.],
Automatic Video Classification: A Survey of the Literature,
SMC-C(38), No. 3, May 2008, pp. 416-430.
IEEE DOI
Survey, Video Analysis. Groups papers according to whether they mostly use Text (captions), Audio, or Visual analysis. (Longest list is visual.) Color, Shot, Object, or Motion based analysis. Good review of the field. Discusses other video processing techniques.
Schonfeld, D., Shan, C., Tao, D., Wang, L., (Eds.)
Video Search and Mining,
Springer2010, ISBN: 978-3-642-12899-8
WWW Link. Survey, Video Mining. Buy this book: Video Search and Mining (Studies in Computational Intelligence)
Hu, W.M., Xie, N., Li, L., Zeng, X., Maybank, S.J.,
A Survey on Visual Content-Based Video Indexing and Retrieval,
SMC-C(41), No. 6, November 2011, pp. 797-819.
IEEE DOI
Survey, CBVIR. Survey, Video Database. Survey, Retrieval. General strategies, scene segmentation, key frames, shot boundary detection, query interfaces, browsing, etc.
Chang, S.F.,
Exploring Functionalities in the Compressed Image Video Domain,
Surveys(27), No. 4, December 1995, pp. 573-575. Survey, Image Retrieval.
Zhang, H.J., Tian, Q.,
Digital Video Analysis and Recognition for Content-Based Access,
Surveys(27), No. 4, December 1995, pp. 643-644. Survey, Image Retrieval.
Lienhart, R.[Rainer],
Reliable Transition Detection in Videos: A Survey And Practitioner's Guide,
IJIG(1), No. 3, July 2001, pp. 469-486.
Survey, Shot Detection.
Section, Multiple Entries: 20.4.5.5 Survey, Comparison, Evaluation, of Segmentation and Cut Detection, Summarization Chapter Contents (Back)
Survey, Cut Detection. Evaluation, Cut Detection.
Ahanger, G., Little, T.D.C.,
A Survey of Technologies for Parsing and Indexing Digital Video,
JVCIR(7), No. 1, March 1996, pp. 28-43. Survey, Video Parsing.
Brunelli, R., Mich, O., Modena, C.M.,
A Survey on the Automatic Indexing of Video Data,
JVCIR(10), No. 2, June 1999, pp. 78-112.
Survey, Video Indexing.
Koprinska, I.[Irena], Carrato, S.[Sergio],
Temporal video segmentation: A survey,
SP:IC(16), No. 5, January 2001, pp. 477-500.
Elsevier DOI
Survey, Video Indexing.
Money, A.G.[Arthur G.], Agius, H.[Harry],
Video summarisation: A conceptual framework and survey of the state of the art,
JVCIR(19), No. 2, February 2008, pp. 121-143.
Elsevier DOI
Survey, Video Summarization. Video summaries; Video summarisation; Video content; Survey; Conceptual framework; User based information; Contextual information
Wu, F.[Fei], Han, Y.H.[Ya-Hong], Liu, X.[Xiang], Shao, J.[Jian],
The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey,
MultInfoRetr(1), No. 1, April 2012, pp. 3-15.
WWW Link.
Survey, Image Retrieval.
Wang, H.L.[Hua-Lu], Divakaran, A.[Ajay], Vetro, A.[Anthony], Chang, S.F.[Shih-Fu], Sun, H.F.[Hui-Fang],
Survey of compressed-domain features used in audio-visual indexing and analysis,
JVCIR(14), No. 2, June 2003, pp. 150-183.
Elsevier DOI
Survey, Image Retrieval.
Apostolidis, E.[Evlampios], Adamantidou, E.[Eleni], Metsai, A.I.[Alexandros I.], Mezaris, V.[Vasileios], Patras, I.[Ioannis],
Video Summarization Using Deep Neural Networks: A Survey,
PIEEE(109), No. 11, November 2021, pp. 1838-1863.
IEEE DOI
Survey, Summarization. Training data, Deep learning, Taxonomy, Systematics, Recurrent neural networks, VIdeo sequences, Neural networks, video summarization
Otani, M.[Mayu], Song, Y.[Yale], Wang, Y.[Yang],
Video Summarization Overview,
FTCGV(13), No. 4, 2022, pp. 284-335.
DOI Link
Survey, Summarization.
Baskurt, K.B.[Kemal Batuhan], Samet, R.[Refik],
Video synopsis: A survey,
CVIU(181), 2019, pp. 26-38.
Elsevier DOI
Survey, Video Synopsis. Video surveillance, Video processing, Video synopsis, Motion detection, Object tracking, Optimization, Stitching
Vivekraj, V.K., Sen, D.[Debashis], Raman, B.[Balasubramanian],
Video Skimming: Taxonomy and Comprehensive Survey,
Surveys(52), No. 5, October 2019, pp. Article No 106.
DOI Link
Survey, Video Skimming.
Schiappa, M.C.[Madeline C.], Rawat, Y.S.[Yogesh S.], Shah, M.[Mubarak],
Self-Supervised Learning for Videos: A Survey,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link
Survey, Video Understanding. Survey, Self-Supervised Learning. video understanding, zero-shot learning, visual-language models, deep learning, multimodal learning
Irene, S., John-Prakash, A., Rhymend-Uthariaraj, V.,
Person search over security video surveillance systems using deep learning methods: A review,
IVC(143), 2024, pp. 104930.
Elsevier DOI
Survey, Person Search. Person search, Person retrieval, Deep learning, Person re-identification, Text based person search, Feature representation
Auda, J.[Jonas], Gruenefeld, U.[Uwe], Faltaous, S.[Sarah], Mayer, S.[Sven], Schneegass, S.[Stefan],
A Scoping Survey on Cross-Reality Systems,
Surveys(56), No. 4, October 2023, pp. xx-yy.
DOI Link
Survey, Augmented Reality. reality-virtuality continuum, collaboration, virtual reality, augmented reality, transitional interfaces, augmented virtuality
Wen, Y., Zhu, X., Rodrigues, J.J.P.C., Chen, C.W.,
Cloud Mobile Media: Reflections and Outlook,
MultMed(16), No. 4, June 2014, pp. 885-902.
IEEE DOI
Survey, Cloud Media. Cloud computing
Tzelepis, C.[Christos], Ma, Z.G.[Zhi-Gang], Mezaris, V.[Vasileios], Ionescu, B.[Bogdan], Kompatsiaris, I.[Ioannis], Boato, G.[Giulia], Sebe, N.[Nicu], Yan, S.C.[Shui-Cheng],
Event-Based Media Processing and Analysis: A Survey of the Literature,
IVC(53), No. 1, 2016, pp. 3-19.
Elsevier DOI
Survey, Media Processing. Event-based media processing and analysis
Kofler, C.[Christoph], Larson, M.[Martha], Hanjalic, A.[Alan],
User Intent in Multimedia Search: A Survey of the State of the Art and Future Challenges,
Surveys(48), No. 3, February 2016, pp. 36.
DOI Link
Survey, Multimedia Retrieval. survey of multimedia information retrieval research directed at the problem of enabling search engines to respond to user intent.
Lee, P.Y.[Pui Yi], Loh, W.P.[Wei Ping], Chin, J.F.[Jeng Feng],
Feature selection in multimedia: The state-of-the-art review,
IVC(67), No. 1, 2017, pp. 29-42.
Elsevier DOI
Survey, Multimedia. Feature selection
Lawton, G.,
Making virtual reality more accessible,
Computer(39), No. 6, June 2006, pp. 12-15.
IEEE DOI
Survey, Virtual Reality. Survey of VR
Gutiérrez A., M.A.[Mario A.], Vexo, F.[Frédéric], Thalmann, D.[Daniel],
Stepping into Virtual Reality,
Springer2008, ISBN: 978-1-84800-116-9
WWW Link. Survey, Virtual Reality. Buy this book: Stepping into Virtual Reality
Liu, H.Y.[Huai-Yu], Bowman, M.[Mic], Chang, F.[Francis],
Survey of state melding in virtual worlds,
Surveys(44), No. 4, August 2012, pp. Article No 21.
DOI Link
Survey, Virtual Reality. State melding is the core of creating this illusion of a shared reality.
Ribeiro-de Oliveira, T.[Taina], Rodrigues, B.B.[Brenda Biancardi], Moura-da Silva, M.[Matheus], Spinasse, R.A.N.[Rafael Antonio N.], Ludke, G.G.[Gabriel Giesen], Gaudio, M.R.S.[Mateus Ruy Soares], Gomes, G.I.R.[Guilherme Iglesias Rocha], Cotini, L.G.[Luan Guio], da Silva-Vargens, D.[Daniel], Queiroz-Schimidt, M.[Marcelo], Andreao, R.V.[Rodrigo Varejao], Mestria, M.[Mario],
Virtual Reality Solutions Employing Artificial Intelligence Methods: A Systematic Literature Review,
Surveys(55), No. 10, February 2023, pp. xx-yy.
DOI Link
Survey, Augmented Reality. literature review, Virtual reality, artificial intelligence, Industry 4.0
Section, Multiple Entries: 20.6.3 Multimedia, Audio-Visual Communications, Survey Chapter Contents (Back)
Survey, Multimedia. Multimedia.
Fox, E.A.[Edward A.],
Advances in Interactive Digital Multimedia Systems,
Computer(24), No. 10, Octobber 1991, pp. 9-21. Survey, Multimedia. Survey of the basic techniques and issues.
Kerr, G.[Gordon],
A review of fully interactive video on demand,
SP:IC(8), No. 3, April 1996, pp. 173-190.
Elsevier DOI
Survey, Video on Demand.
Vandalore, B.[Bobby], Feng, W.C.[Wu-Chi], Jain, R.[Raj], Fahmy, S.[Sonia],
A Survey of Application Layer Techniques for Adaptive Streaming of Multimedia,
RealTimeImg(7), No. 3, June 2001, pp. 221-235.
DOI Link
Survey, Multimedia.
Dasu, A.R., Panchanathan, S.,
A survey of media processing approaches,
CirSysVideo(12), No. 8, August 2002, pp. 633-645.
IEEE Top Reference.
Survey, Multimedia.
Frias-Martinez, E., Chen, S.Y., Liu, X.,
Survey of Data Mining Approaches to User Modeling for Adaptive Hypermedia,
SMC-C(36), No. 6, November 2006, pp. 734-749.
IEEE DOI
Survey, Data Mining.
Gao, W.[Wen], Tian, Y.H.[Yong-Hong], Huang, T.J.[Tie-Jun], Yang, Q.A.[Qi-Ang],
Vlogging: A survey of videoblogging technology on the web,
Surveys(42), No. 4, June 2010, pp. xx-yy.
Survey, Software.
Zhao, Q.P.[Qin-Ping],
10 Scientific Problems in Virtual Reality,
CACM(54), No. 2, February 2011, pp. 116-118.
DOI Link
Survey, Virtual Reality. Virtual Reality was one of the 14 Grand Challenges identified as awaiting engineering solutions for the 21st century announced in 2008 by the U.S. National Academy of Engineering. Here, I explore 10 related open VR challenges
Hu, S.M.[Shi-Min], Chen, T.[Tao], Xu, K.[Kun], Cheng, M.M.[Ming-Ming], Martin, R.R.[Ralph R.],
Internet visual media processing: A survey with graphics and vision applications,
VC(29), No. 5, May 2013, pp. 393-405.
WWW Link.
Survey, Multimedia.
Wiriyathammabhum, P.[Peratham], Summers-Stay, D.[Douglas], Fermüller, C.[Cornelia], Aloimonos, Y.[Yiannis],
Computer Vision and Natural Language Processing: Recent Approaches in Multimedia and Robotics,
Surveys(49), No. 4, February 2017, pp. Article No 71.
DOI Link
Survey, Multimedia.
Deldjoo, Y.[Yashar], di Noia, T.[Tommaso], Merra, F.A.[Felice Antonio],
A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial Networks,
Surveys(54), No. 2, March 2021, pp. xx-yy.
DOI Link
Survey, Recommender. privacy, Recommender systems, adversarial perturbation, generative adversarial network, robustness, security, min-max game
Ganjam, A., Zhang, H.,
Internet Multicast Video Delivery,
PIEEE(93), No. 1, January 2005, pp. 159-170.
IEEE DOI
Survey, Video Servers.
Park, S.[Soohong], Mannens, E.[Erik], van de Walle, R.[Rik], Soderberg, J.[Joakim], Adams, G.[Glenn], Le Hegaret, P.[Philippe], Hong, C.S.[Choong Seon],
Video in the Web: Technical Challenges and Standardization,
MultMedMag(17), No. 4, October-December 2010, pp. 90-93.
IEEE DOI
Survey, Video Streaming.
Coileáin, D.Ó.[Diarmuid Ó], O'Mahony, D.[Donal],
Accounting and Accountability in Content Distribution Architectures: A Survey,
Surveys(47), No. 4, July 2015, Article No. 59.
DOI Link
Survey, Video Servers.
Zolfaghari, B.[Behrouz], Srivastava, G.[Gautam], Roy, S.[Swapnoneel], Nemati, H.R.[Hamid R.], Afghah, F.[Fatemeh], Koshiba, T.[Takeshi], Razi, A.[Abolfazl], Bibak, K.[Khodakhast], Mitra, P.[Pinaki], Rai, B.K.[Brijesh Kumar],
Content Delivery Networks: State of the Art, Trends, and Future Roadmap,
Surveys(53), No. 2, April 2020, pp. xx-yy.
DOI Link
Survey, Content Delivery. trend, survey, Content delivery network
Shirai, Y.,
Robot Vision,
FGCS(1), No. 5, September 1985, pp. 325-352. Survey, Industrial Applications. Industrial Vision, Survey. A survey of computer techniques used in industrial applications especially in Japan. Noticeably simple techniques that work.
Malamas, E.N.[Elias N.], Petrakis, E.G.M.[Euripides G. M.], Zervakis, M.E.[Michalis E.], Petit, L.[Laurent], Legat, J.D.[Jean-Didier],
A survey on industrial vision systems, applications and tools,
IVC(21), No. 2, February 2003, pp. 171-188.
Elsevier DOI
Survey, Industrial Applications.
Billingsley, J.[John], Bradbeer, R.[Robin], (Eds.)
Mechatronics and Machine Vision in Practice,
Springer2008, ISBN: 978-3-540-74026-1.
WWW Link. Survey, Robotics.
Editor Introduction,
Robotics and Automation,
Computer(15), No. 12, December 1982, Special issue. Survey, Automation. Automation, Survey. Contains several survey type articles and 2 papers on robotics control and languages.
Shi, Q.[Quan], Xi, N.[Ning],
Develop a Non-contact Automated Dimensional Inspection System for Automotive Manufacturing Industry,
RPCS(1), No. 2, June 2008, pp. 76-83.
WWW Link.
Survey, Inspection.
Diers, J.[Jan], Pigorsch, C.[Christian],
A Survey of Methods for Automated Quality Control Based on Images,
IJCV(131), No. 10, October 2023, pp. 2553-2581.
Springer DOI
Survey, Quality Control.
Section, Multiple Entries: 20.7.3.1 Inspection Systems -- General, Survey, Review Chapter Contents (Back)
Application, Inspection. Survey, Inspection.
Chin, R.T.[Roland T.],
Automated Visual Inspection Techniques and Applications: A Bibliography,
PR(15), No. 4, 1982, pp. 343-357.
Elsevier DOI Survey, Inspection. A bibliography of inspection papers broken into various categories. Overview, general, rationales for automating inspection, system components and design considerations, commercially available, pc patterns, microcircuit photomasks, chip inspection and alignment for bonding, inspection of other electrical and electronic assemblies, auto parts, metal processing industry, fabric, radiographic imaging, other applications. There is no evaluation of the various papers.
Chin, R.T.[Roland T.],
Automated Visual Inspection: 1981 to 1987,
CVGIP(41), No. 3, March 1988, pp. 346-381.
Elsevier DOI Survey, Inspection. Updated papers from the first one.
Chin, R.T., and Harlow, C.A.,
Automated Visual Inspection: A Survey,
PAMI(4), No. 6, November, 1982, pp. 557-573. Survey, Inspection. A large number (200) of references to work in the mid to late 1970s.
Newman, T.S., Jain, A.K.,
A Survey of Automated Visual Inspection,
CVIU(61), No. 2, March 1995, pp. 231-262.
DOI Link Survey, Inspection. An extensive survey of how to do visual inspection and how to analyze the results.
Shirvaikar, M.V.[Mukul V.],
Trends in automated visual inspection,
RealTimeIP(1), No. 1, October 2006, pp. 41-43.
Springer DOI
Survey, Inspection.
Moganti, M., Ercal, F., Dagli, C.H., Tsunekawa, S.,
Automatic PCB Inspection Algorithms: A Survey,
CVIU(63), No. 2, March 1996, pp. 287-313.
DOI Link Survey, Inspection.
Thibadeau, R.H.[Robert H.],
Printed Circuit Board inspection,
CMU-RI-TR-81-8, 1981, CMU Robotics Institute. Survey, Inspection. Inspection, PCB - Survey. Survey of various techniques - expand contract and see the different areas, table lookup of patterns (difficult), spatial entropy, run lengths (most are in certain range, others are errors), FFT analysis, analysis of edge coding. The goal seems to be to try many alternatives and use results to analyze defects.
Ngan, H.Y.T.[Henry Y.T.], Pang, G.K.H.[Grantham K.H.], Yung, N.H.C.[Nelson H.C.],
Automated fabric defect detection: A review,
IVC(29), No. 7, June 2011, pp. 442-458.
Elsevier DOI
Survey, Fabric Defects. Fabric defect detection; Textile; Motif-based; Automation; Quality control; Manufacturing
Jiménez, A.R., Jain, A.K., Ceres, R., Pons, J.L.,
Automatic fruit recognition: A survey and new results using Range/Attenuation images,
PR(32), No. 10, October 1999, pp. 1719-1736.
Elsevier DOI Survey, Inspection.
Martineau, M.[Maxime], Conte, D.[Donatello], Raveaux, R.[Romain], Arnault, I.[Ingrid], Munier, D.[Damien], Venturini, G.[Gilles],
A survey on image-based insect classification,
PR(65), No. 1, 2017, pp. 273-284.
Elsevier DOI
Survey, Insects. Image-based insect recognition
Drouyer, S.[Sébastien],
An 'All Terrain' Crack Detector Obtained by Deep Learning on Available Databases,
IPOL(10), 2020, pp. 105-123.
DOI Link
Survey, Crack Detection. Code, Crack Detection.
Mathavan, S., Kamal, K., Rahman, M.,
A Review of Three-Dimensional Imaging Technologies for Pavement Distress Detection and Measurements,
ITS(16), No. 5, October 2015, pp. 2353-2362.
IEEE DOI
Survey, Pavement Analysis. computer vision
Zharkova, V.[Valentina], Ipson, S.[Stanley], Benkhalil, A.[Ali], Zharkov, S.[Sergei],
Feature Recognition in Solar Images,
AIR(23), No. 3, May 2005, pp. 209-266.
WWW Link.
Survey, Solar Images.
Rocha, A.[Anderson], Scheirer, W.J.[Walter J.], Boult, T.E.[Terrance E.], Goldenstein, S.K.[Siome K.],
Vision of the unseen: Current trends and challenges in digital image and video forensics,
Surveys(43), No. 4, October 2011, pp. xx-yy.
DOI Link
Survey, Video Forensics. Digital images are everywhere: from our cell phones to the pages of our online news sites. How we choose to use digital image processing raises a surprising host of legal and ethical questions that we must address.
Shah, S.[Shishir], Gabriel, E.[Edgar],
Image computing for digital pathology,
ICPR08(1-1).
IEEE DOI
Survey, Pathology.
Chen, C.H., Pau, L.F., and Wang, P.S.P.,
Handbook of Pattern Recognition and Computer Vision, Fourth Edition,
World ScientificOctober 2009. ISBN: 978-981-4273-38-1 Some indexed as: HPCV09 Survey, Pattern Recognition. Buy this book: Handbook of Pattern Recognition and Computer Vision
Chen, C.H., and Wang, P.S.P.,
Handbook of Pattern Recognition and Computer Vision, Third Edition,
World ScientificJanuary 2005. ISBN 978-981-256-105-3. Survey, Pattern Recognition.
HTML Version. Buy this book: Handbook of Pattern Recognition and Computer Vision
Davis, L.S.[Larry S.], (Ed.)
Foundations of Image Understanding,
KluwerBoston, August 2001. ISBN 0-7923-7457-6, Indexed as: FIU01
WWW Link. Survey, Image Understanding. 1. Summation; A. Rosenfeld. 2.
See also Digital Geometry: The Birth of a New Discipline. 3.
See also Digital Topology. 4. Fuzzy Mathematics; J.N. Mordeson. 5.
See also Picture Languages. 6.
See also Parallel Image Processing. 7.
See also Object Representations. 8.
See also Texture Classification and Segmentation. 9.
See also Edge Measures Using Similarity Regions. 10.
See also Relaxation Labeling: 25 Years and Still Iterating. 11.
See also From a robust hierarchy to a hierarchy of robustness. 12.
See also Pyramid Framework for Real-Time Computer Vision, A. 13.
See also On the Computational Modeling of Human Vision. 14.
See also Statistics Explains Geometrical Optical Illusions. 15.
See also Optics for OmniStereo Imaging. 16.
See also Volumetric scene reconstruction from multiple views. Buy this book: Foundations of Image Understanding (The Springer International Series in Engineering and Computer Science)
Kisacanin, B.[Branislav], Pavlovic, V.[Vladimir], Huang, T.S.[Thomas S.], (Eds.)
Real-Time Vision for Human-Computer Interaction,
Springer2005, ISBN: 978-0-387-27697-7.
Springer DOI Survey, HCI.
Earlier:
Preface to Workshop on Real-Time Vision for Human-Computer Interaction,
RealTimeHCI04(150).
IEEE DOI
Buy this book: Real-Time Vision for Human-Computer Interaction
Duda, R.O., Hart, P.E., and Stork, D.G.,
Pattern Classification,
New York: Wiley2001. ISBN: 0-471-05669-3. Second Edition.
HTML Version. Vision Text. Pattern Recognition. Survey, Pattern Recognition. This is a significant revision and expansion of the first half of the classic Duda and Hart book (
See also Pattern Classification and Scene Analysis. ). Buy this book: Pattern Classification (2nd Edition)
Horn, B.K.P.,
Robot Vision,
Cambridge: MIT Press1986. ISBN 0-262-08159-8. (Also McGraw-Hill0-07-030349-5)
PDF File. Survey, Robot Vision. Vision Text. Standard computer vision book. Image formation and image sensing; Binary images: geometrical properties; Regions and images; Topological properties; Segmentation; Continuous Images; Discrete Images; Edges and edge finding; Lightness and color; Reflectance map; Photometric stereo; Shape from Shading; Motion field and optical flow; Photogrammetry and stereo; Pattern classification; Polyhedral objects; Extended gaussina images; Passive navigation and structure from motion; Picking parts out of a bin. Buy this book: Robot Vision (MIT Electrical Engineering and Computer Science)
Wechsler, H.,
Computational Vision,
San Diego: Academic Press1990, 576 pp. Integrated theory of computational vision. Survey, Computational Vision. Computational Vision, Survey.
Davies, E.R.,
Machine Vision: Theory, Algorithms, Practicalities, Third Edition,
Elsevier2005. ISBN: 978-0-12-206093-9
WWW Link. Survey, Machine Vision.
Earlier:
Machine Vision: Theory, Algorithms, Practicalities, Second Edition,
San Diego: Academic Press1996.
Earlier:
Machine Vision: Theory, Algorithms, Practicalities,
San Diego: Academic Press1990, 576 pp. General overview book. Buy this book: Machine Vision : Theory, Algorithms, Practicalities
Fukunaga, K.[Keinosuke],
Introduction to Statistical Pattern Recognition,
(Second Edition), San Diego: Academic Press1990. ISBN 0-12-269851-7. First Edition: 1972. Pattern Recognition. Survey, Pattern Recognition. The standard reference book.
Shapiro, L.G.[Linda G.], Stockman, G.C.[George C.],
Computer Vision,
Prentice Hall2001. ISBN: 013-0307963.
HTML Version. Survey, Computer Vision. Buy this book: Computer Vision
IAPR Tutorials on Topics in 2D Image Analysis, Computer Vision,
Online Book2008.
Survey, Computer Vision.
HTML Version. A collection of various tutorials, such as might be given at a summer school or conference tutorial on topics in Computer Vision.
IAPR Pattern Recognition Education Resources,
Online Book2008.
Survey, Computer Vision.
WWW Link. The most important resources are for students, researchers and educators. Tutorials and surveys, Explanatory text, Online demos, Datasets, Book lists, Free code, Course notes, Lecture slides, Course reading lists, Coursework/homework, A list of course web pages at many universities. Divided into 3 core technology areas and 2 broad families of application areas: 1. Symbolic Pattern Recognition 2. Statistical Pattern Recognition 3. Machine Learning 4. 1D Signal Analysis 5. Computer vision/Image Processing/Machine Vision
See also IAPR: International Association for Pattern Recognition.
Klette, R., Schluens, K., and Koschan, A.F.,
Computer Vision,
SpringerSingapore 1998. In English. ISBN: 981-3083-71-9. survey, Computer Vision.
Earlier: A1, A3, A2: ViewegBraunschweig, Germany, 1996. in German. The English version is revised and expanded. Buy this book: Computer Vision
Hérault, J.[Jeanny],
Vision: Images, Signals and Neural Networks, Models of Neural Processing in Visual Perception,
World ScientificMarch 2010 ISBN: 978-981-4273-68-8 Survey, Human Vision.
HTML Version. Buy this book: Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing)
Vision from the perspective of human visual system applied to computer processing.
Dua, S.[Sumeet], U, R.A.[Rajendra Acharya], Ng, E.Y.K.,
Computational Analysis of the Human Eye with Applications,
World ScientificApril 2011 ISBN: 978-981-4340-29-8 Survey, Human Vision.
HTML Version. Buy this book: Computational Analysis of the Human Eye with Applications
Rosenfeld, A., and Kak, A.C.,
Digital Picture Processing,
Two volumes. New York: Academic Press1982. ISBN 0-12-597301-2.
Earlier: First edition, New York: Academic Press1976. Survey, Image Processing. Image Processing, Survey. The early basic image processing book. Buy this book: Digital Picture Processing: Volume 1 (Computer Science and Applied Mathematics)
Hall, E.L.,
Computer Image Processing and Recognition,
New York: Academic Press1979. Survey, Image Processing. Image Processing, Survey.
Gonzalez, R.C.[Rafael C.], Woods, R.E.[Richard E.],
Digital Image Processing, Third Edition,
Prentice Hall2008. ISBN: 013168728X Buy this book: Digital Image Processing (3rd Edition)
Earlier:
Digital Image Processing, Second Edition,
Prentice Hall2002. ISBN: 0201180758. Survey, Image Processing. Image Processing, Survey.
HTML Version. Instructors Manual:
HTML Version.
Gonzalez, R.C., and Wintz, P.A.,
Digital Image Processing,
Reading, MA: Addison-Wesley1977.
And: Replace A2: Woods, R., Reading, MA: Addison-Wesley1982. And 1992. Survey, Image Processing. Image Processing, Survey.
Pratt, W.K.,
Digital Image Processing (Second Edition),
New York: Wiley1991, 698 pp. ISBN 0-471-85766-1
Earlier:
Digital Image Processing (First Edition),
New York: Wiley1978. Survey, Image Processing. Image Processing, Survey. The first was comprenhensive, but the second leaves out relevant material. Buy this book: Digital Image Processing
Jain, A.K.,
Fundamentals of Digital Image Processing,
Englewood Cliffs, NJ: Prentice Hall1989. Survey, Image Processing. Image Processing, Survey.
Pavlidis, T.,
Algorithms for Graphics and Image Processing,
Rockville MD: Computer Science Press1982. Survey, Algorithms. Algorithms, Survey. Code, Image Processing. The book has basic algorithms for many standard image processing tasks. Topics: Digitization, processing, segmentation, projection, data structures, binary images, contour filling, thinning, curve fitting, surface fitting, 2-D graphics, polygon clipping, 3-D graphics.
Aubert, G.[Gilles], Kornprobst, P.[Pierre],
Mathematical Problems in Image Processing Partial Differential Equations and the Calculus of Variations,
Springer2006, ISBN 978-0-387-32200-1.
Springer DOI
Earlier: SpringerApplied Mathematical Sciences, Vol 147, 2002. ISBN 0-387-95326-4.
HTML Version. Or:
Springer DOI PDE. Variational Approach. Survey, PDE. PDEs applied to Image Processing.
Woolfson, M.M.[Michael Mark],
The Fundamentals of Imaging: From Particles to Galaxies,
World ScientificSeptember 2011 ISBN: 978-1-84816-684-4
 
|
HTML Version. Survey, Imaging.
Text book level discussion, human visual system to cameras, microscopes, telescopes, non-visible spectrum.
Pavlidis, T.,
A Critical Survey of Image Analysis Methods,
ICPR86(502-511), expanded version. Survey, Image Analysis. Image Analysis, Survey. Generally we have been very slow in integrating results into useful programs.
Section, Multiple Entries: 3.6.1 Computer Vision Surveys Chapter Contents (Back)
Survey, Computer Vision.
Huang, T.S., Tretiak, O.J., Tippett, J.T.,
Research in Picture Processing,
OE-OIP65(Chapter 3), 1965. Survey, Image Processing.
Selfridge, O.G.,
Pattern Recognition and Modern Computers,
WJCC55(91-93). Survey, Pattern Recognition.
Forsen, G.,
Processing Visual Data with an Automaton Eye,
PPR68(471-502). Survey, Image Processing.
Rosenfeld, A.[Azriel], and Weszka, J.S.,
Picture Recognition and Scene Analysis,
Computer(9), No. 5, May 1976, pp. 28-38. Survey, Image Processing.
Rosenfeld, A.[Azriel],
Image Analysis: Problems, Progress and Prospects,
PR(17), No. 1, 1984, pp. 3-12.
Elsevier DOI Survey, Image Processing.
And: RCV87(3-12). A review of the state of image analysis at that time.
Rosenfeld, A.[Azriel],
Human and Machine Vision Special Issue,
CVGIP(37), No. 1, January 1987, pp. 1-2. CVGIP(37), No. 2, February 1987. Part II. Survey, Human Vision. Human Vision, Survey.
Aloimonos, Y., and Rosenfeld, A.[Azriel],
Principles of Computer Vision,
HPRIP-CV94(1-15). Survey, Computer Vision.
Earlier:
Computer Vision,
Science(253), September 13, 1991, pp. 1249-1254.
Earlier: A2 only: HPRIP86(355-368). General survey.
Waldrop, M.M.[M. Mitchell],
Computer Vision,
Science(224), No. 4654, 15 June 1984, pp. 1225-1227. Survey, Computer Vision. Short outline of what is happening in computer vision research.
Rosenfeld, A.[Azriel],
Computer Vision: Past, Present, and Future,
IS(57-58), 1991, pp. 241-243. Survey, Computer Vision.
Haralick, R.M.[Robert M.], Shapiro, L.G.,
Glossary Of Computer Vision Terms,
PR(24), No. 1, 1991, pp. 69-93.
Elsevier DOI Survey, Computer Vision.
Haralick, R.M.[Robert M.],
Glossary and Index to Remotely Sensed Image Pattern Recognition Concepts,
PR(5), No. 4, December 1973, pp. 391-403.
Elsevier DOI Survey, Pattern Recognition.
Rosenfeld, A.[Azriel],
Computer Vision: Basic Principles,
PIEEE(76), 1988, pp. 863-868. Survey, Computer Vision.
Rosenfeld, A.[Azriel],
Computer Vision: Signals, Segments, and Structures,
ASSP MAGAZINE(1), No. 1, 1984, pp. 11-18. Survey, Computer Vision.
Rosenfeld, A.[Azriel],
From Image Analysis to Computer Vision: An Annotated Bibliography, 1955-1979,
CVIU(84), No. 2, November 2001, pp. 298-324.
DOI Link Survey, Computer Vision.
Earlier:
From Image Analysis to Computer Vision: Motives, Methods, And Milestones, 1955-1979,
UMD--TR3920R, July 1998.
PS File. Historical development of computer vision.
Rosenfeld, A.[Azriel], Wechsler, H.[Harry],
Pattern recognition: Historical perspective and future directions,
IJIST(11), No. 2, 2000, pp. 101-116.
Survey, Pattern Recognition.
Zavidovique, B.Y.,
First steps of robotic perception: the turning point of the 1990s,
PIEEE(90), No. 7, July 2002, pp. 1094-1112.
IEEE DOI
Survey, Computer Vision.
Rosenfeld, A.[Azriel],
Picture Processing by Computer: Survey,
Surveys(1), No. 3, September 1969, pp. 147-176.
And:
Progress in Picture Processing: 1969-71,
Surveys(5), No. 2, June 1973, pp. 81-108.
And:
Picture Processing: 1972,
CGIP(1), No. 4, December 1972, pp. 394-411.
Elsevier DOI
And:
Picture Processing: 1973,
CGIP(3), No. 2, June 1974, pp. 178-192.
Elsevier DOI
And:
Picture Processing: 1974,
CGIP(4), No. 2, June 1975, pp. 133-151.
Elsevier DOI
And:
Picture Processing: 1975,
CGIP(5), No. 2, June 1976, pp. 215-233.
Elsevier DOI
And:
Picture Processing: 1976,
CGIP(6), No. 2, April 1977, pp. 157-179.
Elsevier DOI
And:
Picture Processing: 1977,
CGIP(7), No. 2, April 1978, pp. 211-236.
Elsevier DOI
And:
Picture Processing: 1978,
CGIP(9), No. 4, April 1979, pp. 354-386.
Elsevier DOI
And:
Picture Processing: 1979,
CGIP(13), No. 1, May 1980, pp. 46-79.
Elsevier DOI
And:
Picture Processing: 1980,
CGIP(16), No. 1, May 1981, pp. 52-89.
Elsevier DOI
And:
Picture Processing: 1981,
CGIP(19), No. 1, May 1982, pp. 35-67.
Elsevier DOI
And:
Picture Processing: 1982,
CVGIP(22), No. 3, June 1983, pp. 339-377.
Elsevier DOI
And:
Picture Processing: 1983,
CVGIP(26), No. 3, June 1984, pp. 347-384.
Elsevier DOI
And:
Picture Processing: 1984,
CVGIP(30), No. 2, May 1985, pp. 189-231.
Elsevier DOI
And:
Picture Processing: 1985,
CVGIP(34), No. 2, May 1986, pp. 204-241.
Elsevier DOI
And:
Picture Processing: 1986,
CVGIP(38), No. 2, May 1987, pp. 147-213.
Elsevier DOI
And:
Image Analysis and Computer Vision: 1987,
CVGIP(42), No. 2, May 1988, pp. 234-281.
Elsevier DOI
And:
Image Analysis and Computer Vision: 1988,
CVGIP(46), No. 2, May 1989, pp. 196-250.
Elsevier DOI
And:
Image Analysis and Computer Vision: 1989,
CVGIP(50), No. 2, May 1990, pp. 188-230.
Elsevier DOI
And:
Image Analysis and Computer Vision: 1990,
CVGIP(53), No. 3, May 1991, pp. 322-365.
Elsevier DOI
And:
Image Analysis and Computer Vision: 1991,
CVGIP(55), No. 3, May 1992, pp. 349-373.
Elsevier DOI
And:
Image Analysis and Computer Vision: 1992,
CVGIP(58), No. 1, July 1993, pp. 85-135.
DOI Link
And:
Image-Analysis and Computer Vision: 1993,
CVGIP(59), No. 3, May 1994, pp. 367-404.
DOI Link
And:
Image-Analysis and Computer Vision: 1994,
CVIU(62), No. 1, July 1995, pp. 90-131.
DOI Link
And:
Image-Analysis and Computer Vision: 1995,
CVIU(63), No. 3, May 1996, pp. 568-602.
WWW Link.
DOI Link
And:
Image-Analysis and Computer Vision: 1996,
UMDTR3733, January 1997.
WWW Link.
And:
Image Analysis and Computer Vision: 1997,
CVIU(70), No. 2, May 1998, pp. 239-284.
And: UMD--TR3861, January 1998.
WWW Link.
And:
Image Analysis and Computer Vision: 1998,
CVIU(74), No. 1, April 1999, pp. 36-95.
And: UMD--TR3974, January 1999.
DOI Link
WWW Link.
And:
Image Analysis and Computer Vision: 1999,
CVIU(78), No. 2, May 2000, pp. 222-302.
And: UMD--TR4094, January 2000.
DOI Link
WWW Link. Survey, Bibliography. Bibliography. The standard printed bibliography for computer vision. All of these also appeared as UMD reports.
Price, K.E.[Keith E.],
Annotated Computer Vision Bibliography,
Online Book1997. USC Computer Vision Survey, Bibliography. Bibliography. The self reference to this work. Over 87,000 entries.
HTML Version. or
HTML Version. Also included in the Computer Science Bibliographies:
HTML Version.
Uhr, L.[Leonard],
Review of psychological processes in pattern recognition,
CGIP(3), No. 4, December 1974, pp. 359-361.
Elsevier DOI
Survey, Psychological Process.
Thacker, N.A.[Neil A.], Clark, A.F.[Adrian F.], Barron, J.L.[John L.], Beveridge, J.R.[J. Ross], Courtney, P.[Patrick], Crum, W.R.[William R.], Ramesh, V.[Visvanathan], Clark, C.[Christine],
Performance characterization in computer vision: A guide to best practices,
CVIU(109), No. 3, March 2008, pp. 305-334.
Elsevier DOI
Survey, Performance Evaluation. Performance assessment; Performance evaluation; Vision system design
Shi, W.R.[Wei-Ren], Li, Z.J.[Zuo-Jin], Shi, X.[Xin], Zhong, Z.[Zhi],
A Survey of Biologically Inspired Image Processing for Objects Recognition,
IJIG(9), No. 4, October 2009, pp. 495-510.
DOI Link
Survey, Human Vision. Survey of existing techniques where biological parallels are working and discusses places where there are serious problem (i.e. bringing knowledge to bear).
Suen, C.Y., Dumont, N., Dyson, M., Tai, Y.C., Lu, X.,
Evaluation of Fonts for Digital Publishing and Display,
ICDAR11(1424-1436).
IEEE DOI
Survey, Fonts. Human visual system analysis. Reading, comprenhension, eye-strain.
Reproducible Research,
OnlineJanuary 2009.
WWW Link. Survey, Evaluation. Evaluation, General.
A site for gathering information regarding reproducible research, which while common in some fields has been lacking in many parts of signal and iamge processing.
Nagy, G., and Wagle, S.,
Geographic Data Processing,
Surveys(11), No. 2, June 1979, pp. 139-181.
WWW Link. Survey, GIS. Discusses GIS, data processing, automated cartography, computational geometry, etc.
Gil, Y.[Yolanda], Pierce, S.A.[Suzanne A.], Babaie, H.[Hassan], Banerjee, A.[Arindam], Borne, K.[Kirk], Bust, G.[Gary], Cheatham, M.[Michelle], Ebert-Uphoff, I.[Imme], Gomes, C.[Carla], Hill, M.[Mary], Horel, J.[John], Hsu, L.[Leslie], Kinter, J.[Jim], Knoblock, C.[Craig], Krum, D.[David], Kumar, V.[Vipin], Lermusiaux, P.[Pierre], Liu, Y.[Yan], North, C.[Chris], Pankratius, V.[Victor], Peters, S.[Shanan], Plale, B.[Beth], Pope, A.[Allen], Ravela, S.[Sai], Restrepo, J.[Juan], Ridley, A.[Aaron], Samet, H.[Hanan], Shekhar, S.[Shashi],
Intelligent Systems for Geosciences: An Essential Research Agenda,
CACM(62), No. 1, January 2019, pp. 76-84.
DOI Link
Survey, Geoscience.
Rusnák, M.[Miloš], Goga, T.[Tomáš], Michaleje, L.[Lukáš], Michalková, M.Š.[Monika Šulc], Mácka, Z.[Zdenek], Bertalan, L.[László], Kidová, A.[Anna],
Remote Sensing of Riparian Ecosystems,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link
Survey, Remote Sensing. Review of many articles on riparian analysis.
Jasani, B., Pesaresi, M., Schneiderbauer, S., Zeug, G., (Eds.)
Remote Sensing from Space: Supporting International Peace and Security,
Springer-Verlag2009. ISBN: 978-1-4020-8483-6
WWW Link. Survey, Remote Sensing. Buy this book: Remote Sensing from Space: Supporting International Peace and Security
An overview of what is available.
Toth, C.[Charles], Józków, G.[Grzegorz],
Remote sensing platforms and sensors: A survey,
PandRS(115), No. 1, 2016, pp. 22-36.
Elsevier DOI
Survey, Sensors. Remote sensing
You, Y.[Yanan], Cao, J.Y.[Jing-Yi], Zhou, W.L.[Wen-Li],
A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link
Survey, Change Detection.
Rolnick, D.[David], Donti, P.L.[Priya L.], Kaack, L.H.[Lynn H.], Kochanski, K.[Kelly], Lacoste, A.[Alexandre], Sankaran, K.[Kris], Ross, A.S.[Andrew Slavin], Milojevic-Dupont, N.[Nikola], Jaques, N.[Natasha], Waldman-Brown, A.[Anna], Luccioni, A.S.[Alexandra Sasha], Maharaj, T.[Tegan], Sherwin, E.D.[Evan D.], Mukkavilli, S.K.[S. Karthik], Kording, K.P.[Konrad P.], Gomes, C.P.[Carla P.], Ng, A.Y.[Andrew Y.], Hassabis, D.[Demis], Platt, J.C.[John C.], Creutzig, F.[Felix], Chayes, J.[Jennifer], Bengio, Y.[Yoshua],
Tackling Climate Change with Machine Learning,
Surveys(55), No. 2, February 2023, pp. xx-yy.
DOI Link
Survey, Climate Change. artificial intelligence, adaptation, Climate change, machine learning, mitigation
Williams, K.[Keith], Olsen, M.J.[Michael J.], Roe, G.V.[Gene V.], Glennie, C.L.[Craig L.],
Synthesis of Transportation Applications of Mobile LIDAR,
RS(5), No. 9, 2013, pp. 4652-4692.
DOI Link
Survey, LIDAR Sensors. On mobile platforms.
Massari, C.[Christian], Modanesi, S.[Sara], Dari, J.[Jacopo], Gruber, A.[Alexander], de Lannoy, G.J.M.[Gabrielle J. M.], Girotto, M.[Manuela], Quintana-Seguí, P.[Pere], Le Page, M.[Michel], Jarlan, L.[Lionel], Zribi, M.[Mehrez], Ouaadi, N.[Nadia], Vreugdenhil, M.[Mariëtte], Zappa, L.[Luca], Dorigo, W.[Wouter], Wagner, W.[Wolfgang], Brombacher, J.[Joost], Pelgrum, H.[Henk], Jaquot, P.[Pauline], Freeman, V.[Vahid], Volden, E.[Espen], Prieto, D.F.[Diego Fernandez], Tarpanelli, A.[Angelica], Barbetta, S.[Silvia], Brocca, L.[Luca],
A Review of Irrigation Information Retrievals from Space and Their Utility for Users,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link
Survey, Irrigation.
Hemati, M.[Mohammad_Ali], Hasanlou, M.[Mahdi], Mahdianpari, M.[Masoud], Mohammadimanesh, F.[Fariba],
A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link
Survey, Landsat Change Detection.
Li, D.L.[Dao-Liang], Zhang, P.[Pan], Chen, T.[Tao], Qin, W.[Wei],
Recent Development and Challenges in Spectroscopy and Machine Vision Technologies for Crop Nitrogen Diagnosis: A Review,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link
Survey, Nitrogen.
Reinermann, S.[Sophie], Asam, S.[Sarah], Kuenzer, C.[Claudia],
Remote Sensing of Grassland Production and Management: A Review,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link
Survey, Grasslands.
Zhang, N.[Ning], Yang, G.J.[Gui-Jun], Pan, Y.C.[Yu-Chun], Yang, X.D.[Xiao-Dong], Chen, L.P.[Li-Ping], Zhao, C.J.[Chun-Jiang],
A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link
Survey, Plant Disease.
Demarquet, Q.[Quentin], Rapinel, S.[Sébastien], Dufour, S.[Simon], Hubert-Moy, L.[Laurence],
Long-Term Wetland Monitoring Using the Landsat Archive: A Review,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link
Survey, Wetlands.
Section, Multiple Entries: 23.7 Photogrammetry, Books on Photogrammetry Chapter Contents (Back)
Survey, Photogrammetry. Photogrammetry.
Mikhail, E.M.[Edward M.], Bethel, J.S.[James S.], McGlone, J.C.[J. Chris],
Introduction to Modern Photogrammetry,
WileyMarch 2001. ISBN: 0471309249 Survey, Photogrammetry. The standard reference. Buy this book: Introduction to Modern Photogrammetry
Yang, H.B.[Hai-Bo], Kong, J.L.[Jia-Lin], Hu, H.H.[Hui-Hui], Du, Y.[Yao], Gao, M.[Meiyan], Chen, F.[Fei],
A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link
Survey, Water Quality.
Mayer, H., Baumgartner, A., Steger, C.T.,
CVonline: Road Extraction from Aerial Imagery,
CV-Online1998.
WWW Link. Survey, Roads. Survey of road extraction work.
Jia, J.X.[Jian-Xin], Sun, H.B.[Hai-Bin], Jiang, C.H.[Chang-Hui], Karila, K.[Kirsi], Karjalainen, M.[Mika], Ahokas, E.[Eero], Khoramshahi, E.[Ehsan], Hu, P.[Peilun], Chen, C.[Chen], Xue, T.R.[Tian-Ru], Wang, T.H.[Ting-Huai], Chen, Y.W.[Yu-Wei], Hyyppä, J.[Juha],
Review on Active and Passive Remote Sensing Techniques for Road Extraction,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link
Survey, Roads.
Tao, Y.D.[Yu-Dong], Yang, C.[Chuang], Wang, T.Y.[Tian-Yi], Coltey, E.[Erik], Jin, Y.X.[Yan-Xiu], Liu, Y.H.[Ying-Hao], Jiang, R.[Renhe], Fan, Z.[Zipei], Song, X.[Xuan], Shibasaki, R.[Ryosuke], Chen, S.C.[Shu-Ching], Shyu, M.L.[Mei-Ling], Luis, S.[Steven],
A Survey on Data-Driven COVID-19 and Future Pandemic Management,
Surveys(55), No. 7, December 2022, pp. xx-yy.
DOI Link
Survey, Covid-10 Management. Data analytics, data visualization, pandemic management, COVID-19
Mayer, H.[Helmut],
Automatic Object Extraction from Aerial Imagery: A Survey Focusing on Buildings,
CVIU(74), No. 2, May 1999, pp. 138-149.
DOI Link Survey, Buildings.
Starzynska-Grzeundefined, M.B.[Malgorzata B.], Roussel, R.[Robin], Jacoby, S.[Sam], Asadipour, A.[Ali],
Computer Vision-Based Analysis of Buildings and Built Environments: A Systematic Review of Current Approaches,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link
Survey, Building Analysis. machine learning, computer vision, image data, built environment, Architecture
Amitrano, D.[Donato], di Martino, G.[Gerardo], di Simone, A.[Alessio], Imperatore, P.[Pasquale],
Flood Detection with SAR: A Review of Techniques and Datasets,
RS(16), No. 4, 2024, pp. 656.
DOI Link Survey, Flood Detection.
Zhao, C.Y.[Chao-Ying], Lu, Z.[Zhong],
Remote Sensing of Landslides: A Review,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link
Survey, Landslides.
Soergel, U.[Uwe], (Ed.)
Radar Remote Sensing of Urban Areas,
Springer-Verlag2010. ISBN: 978-90-481-3750-3 Survey, SAR.
WWW Link.
Buy this book: Radar Remote Sensing of Urban Areas (Remote Sensing and Digital Image Processing)
Techniqes and methods for extraction of urban features (building, vehicles,
etc.) from SAR data.
Gomes, L.[Leonardo], Bellon, O.R.P.[Olga Regina Pereira], Silva, L.[Luciano],
3D reconstruction methods for digital preservation of cultural heritage: A survey,
PRL(50), No. 1, 2014, pp. 3-14.
Elsevier DOI
Survey, Cultural Heritage. 3D reconstruction
Ardito, C.[Carmelo], Buono, P.[Paolo], Costabile, M.F.[Maria Francesca], Desolda, G.[Giuseppe],
Interaction with Large Displays: A Survey,
Surveys(47), No. 3, April 2015, pp. Article No 46.
DOI Link
Survey, Displays. Large interactive displays are increasingly placed in public (or semipublic) locations, including museums, shops, various city settings, and offices. This article discusses the evolution of such displays by looking at their use.
Yang, S.[Su], Xu, S.[Shishuo], Huang, W.[Wei],
3D Point Cloud for Cultural Heritage: A Scientometric Survey,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link
Survey, Cultural Heritage.
Moustaka, V.[Vaia], Vakali, A.[Athena], Anthopoulos, L.G.[Leonidas G.],
A Systematic Review for Smart City Data Analytics,
Surveys(51), No. 5, January 2019, pp. Article No 103.
DOI Link
Survey, Smart City.
Habibzadeh, H.[Hadi], Kaptan, C.[Cem], Soyata, T.[Tolga], Kantarci, B.[Burak], Boukerche, A.[Azzedine],
Smart City System Design: A Comprehensive Study of the Application and Data Planes,
Surveys(51), No. 1, February 2019, pp. Article No 41.
DOI Link
Survey, Smart City.
Section, Multiple Entries: 24.4.5 DEM, DSM, DTM, Evaluations, Valdiation, Surveys, Overviews Chapter Contents (Back)
Survey, Terrain. Terrain. Evaluation, DEM. DEM, Evaluation. DSM, Evaluation. DTM, Evaluation. Digital Terrain Map.
See also Error Analysis, Evaluation, Performance Analysis of Computation Methods.
Mach, R., Petschek, P.,
Visualization of Digital Terrain and Landscape Data: A Manual,
Springer2007. ISBN 978-3-540-30490-6.
WWW Link. Survey, Visualization. Buy this book: Visualization of Digital Terrain and Landscape Data: A Manual
Nguyen, T.[Teo], Liquet, B.[Benoît], Mengersen, K.[Kerrie], Sous, D.[Damien],
Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link
Survey, Coral Reefs.
Huang, W.M.[Wei-Min], Liu, X.L.[Xin-Long], Gill, E.W.[Eric W.],
Ocean Wind and Wave Measurements Using X-Band Marine Radar: A Comprehensive Review,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link
Survey, Wind Measurement.
See also Experimental Investigation of Ocean Wave Measurement Using Short-Range K-Band Radar: Dock-Based and Boat-Based Wind Wave Measurements.
Marvasti-Zadeh, S.M.[S. Mojtaba], Goodsman, D.[Devin], Ray, N.[Nilanjan], Erbilgin, N.[Nadir],
Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review,
Surveys(56), No. 4, November 2023, pp. xx-yy.
DOI Link
Survey, Bark Beetle. deep learning, machine learning, remote sensing, early detection, Bark beetles
Du, Q.S.[Qing-Song], Li, G.Y.[Guo-Yu], Chen, D.[Dun], Zhou, Y.[Yu], Qi, S.S.[Shun-Shun], Wang, F.[Fei], Mao, Y.C.[Yun-Cheng], Zhang, J.[Jun], Cao, Y.[Yapeng], Gao, K.[Kai], Wu, G.[Gang], Li, C.Q.[Chun-Qing], Wang, Y.[Yapeng],
Bibliometric Analysis of the Permafrost Research: Developments, Impacts, and Trends,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link
Survey, Permafrost.
Section, Multiple Entries: 24.8.1 ATR -- Survey, Review, Evaluation, General Discussions Chapter Contents (Back)
Survey, ATR. ATR. Evaluation, ATR.
Bhanu, B.,
Automatic Target Recognition: State of the Art Survey,
AeroSys(22), No. 4, July 1986, pp. 364-379. Survey, ATR. ATR, Survey. Application, ATR.
Byrnes, J.[Jim], (Ed.)
Imaging for Detection and Identification,
Springer2007, ISBN: 978-1-4020-5618-5. And ISBN 978-1-4020-5619-2. Survey, ATR. 2 Volumes. Covers: The mathematics and computer science of automatic detection and identification; Image processing techniques for radar and sonar; Detection of anomalies in biomedical and chemical images.
WWW Link. And:
WWW Link. Buy this book: Imaging for Detection and Identification (NATO Science for Peace and Security Series / NATO Science for Peace and Security Series B: Physics and Biophysics)
Jiang, W.[Wen], Wang, Y.P.[Yan-Ping], Li, Y.[Yang], Lin, Y.[Yun], Shen, W.J.[Wen-Jie],
Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link
Survey, Radar ATR.
Daniels, D.J.[David J.],
EM Detection of Concealed Targets,
Wiley-IEEE PressDecember 2009, ISBN: 978-0-470-12169-6
HTML Version. Survey, Concealed Targets. Various methods, survey of techniques. Buy this book: EM Detection of Concealed Targets (Wiley Series in Microwave and Optical Engineering)
Messer, H., Sendik, O.,
A New Approach to Precipitation Monitoring: A critical survey of existing technologies and challenges,
SPMag(32), No. 3, May 2015, pp. 110-122.
IEEE DOI
Survey, Precipitation. Atmospheric measurements
Li, N.[Nan], Wang, Z.[Zhenhui], Chen, X.[Xi], Austin, G.[Geoffrey],
Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview,
RS(11), No. 1, 2019, pp. xx-yy.
DOI Link
Survey, Precipitation.
Byrnes, J.[James], (Ed.)
Unexploded Ordnance Detection and Mitigation,
Springer2009, ISBN: 978-1-4020-9252-7
WWW Link. Survey, UXO. NATO Advanced Study Institute on Unexploded Ordnance Detection and Mitigation Il Ciocco 20 July - 2 August 2008. Buy this book: Unexploded Ordnance Detection and Mitigation (NATO Science for Peace and Security Series B: Physics and Biophysics)
Ghosh, D.[Debashis], Dube, T.[Tulika], Shivaprasad, A.[Adamane],
Script Recognition: A Review,
PAMI(32), No. 12, December 2010, pp. 2142-2161.
IEEE DOI
Survey, Script Recognition. Identify the script before recognition.
Casey, R.G., Lecolinet, E.,
A Survey of Methods and Strategies in Character Segmentation,
PAMI(18), No. 7, July 1996, pp. 690-706.
IEEE DOI
Survey, Character Segmentation. Evaluation, Segmentation. A good bibliography of related papers.
Ribas, F.C., Oliveira, L.S., de Souza Britto, Jr., A.[Alceu], Sabourin, R.,
Handwritten digit segmentation: a comparative study,
IJDAR(16), No. 2, June 2013, pp. 127-137.
Springer DOI
Survey, Digit Segmentation.
Section, Multiple Entries: 25.4.4.1 Chinese Characters, Review, Survey, Evaluations Chapter Contents (Back)
Survey, OCR. Survey, Chinese Characters. OCR. Chinese Character Recognition. Evaluation, OCR.
Liu, C.L.[Cheng-Lin], Jaeger, S.[Stefan], Nakagawa, M.[Masaki],
Online Recognition of Chinese Characters: The State-of-the-Art,
PAMI(26), No. 2, February 2004, pp. 198-213.
IEEE Abstract.
Survey, Chinese Characters. Survey, OCR. Primarily review of work from the 1990s where the constraints of the 1980s are relaxed.
Plötz, T.[Thomas], Fink, G.A.[Gernot A.],
Markov models for offline handwriting recognition: a survey,
IJDAR(12), No. 4, December 2009, pp. xx-yy.
Springer DOI
Survey, Handwriting.
Uchida, S.[Seiichi], Sakoe, H.[Hiroaki],
A Survey of Elastic Matching Techniques for Handwritten Character Recognition,
IEICE(E88-D), No. 8, August 2005, pp. 1781-1790.
DOI Link
Survey, Character Recognition.
Section, Multiple Entries: 25.4.6.2.3 Handwritten Characters, Cursive Script, Surveys, Data, Comparisons, Evaluations Chapter Contents (Back)
Survey, Cursive Script. Handwriting. Online Systems.
Plamondon, R., Srihari, S.N.,
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey,
PAMI(22), No. 1, January 2000, pp. 63-84.
IEEE DOI
Survey, Handwriting. Recognize Handwriting. Part of the 20 year issue. This is the place to start for techniques and status. Very good bibliography of the field.
Impedovo, D., Pirlo, G.,
Zoning methods for handwritten character recognition: A survey,
PR(47), No. 3, 2014, pp. 969-981.
Elsevier DOI
Survey, OCR. Handwritten character recognition
Impedovo, S.[Sebastiano], Pirlo, G., Modugno, R.[Raffaele], Ferrante, A.[Anna],
Zoning Methods for Hand-Written Character Recognition: An Overview,
FHR10(329-334).
IEEE DOI
Survey, OCR. Divide into regions, features of region for character.
Steinherz, T.[Tal], Rivlin, E.[Ehud], Intrator, N.[Nathan],
Offline Cursive Script Word Recognition: A Survey,
IJDAR(2), No. 2/3, 1999, pp. 90-110.
Survey, Handwriting.
Vinciarelli, A.[Alessandro],
A survey on off-line Cursive Word Recognition,
PR(35), No. 7, July 2002, pp. 1433-1446.
Elsevier DOI
Survey, Handwriting.
Giotis, A.P.[Angelos P.], Sfikas, G.[Giorgos], Gatos, B.[Basilis], Nikou, C.[Christophoros],
A survey of document image word spotting techniques,
PR(68), No. 1, 2017, pp. 310-332.
Elsevier DOI
Survey, Word Spotting.
Earlier: A1, A2, A4, A3:
Shape-based word spotting in handwritten document images,
ICDAR15(561-565)
IEEE DOI
Word spotting handwritten text
Likforman-Sulem, L.[Laurence], Zahour, A.[Abderrazak], Taconet, B.[Bruno],
Text line segmentation of historical documents: a survey,
IJDAR(9), No. 2-4, April 2007, pp. 123-138.
Springer DOI
Survey, Text Extraction.
Tappert, C.C., Suen, C.Y., and Wakahara, T.,
The State of the Art in On-Line Handwriting Recognition,
PAMI(12), No. 8, August 1990, pp. 787-808.
IEEE DOI
And: IBMRC 14045.
And:
On-Line Handwriting Recognition: A Survey,
ICPR88(II: 1123-1132).
IEEE DOI
Survey, Handwriting. Handwriting, Survey.
Nouboud, F., Plamondon, R.,
On-Line Recognition of Handprinted Characters: Survey and Beta Tests,
PR(23), No. 9, 1990, pp. 1031-1044.
Elsevier DOI
Earlier: A2, A1:
Online character recognition system using string comparison processor,
ICPR90(I: 460-463).
IEEE DOI
Survey, Handwriting.
Impedovo, D.[Donato], Pirlo, G.[Giuseppe],
Automatic Signature Verification: The State of the Art,
SMC-C(38), No. 5, September 2008, pp. 609-635.
IEEE DOI
Survey, Signatures.
Earlier:
Verification of Handwritten Signatures: an Overview,
CIAP07(191-196).
IEEE DOI
Diaz, M.[Moises], Ferrer, M.A.[Miguel A.], Impedovo, D.[Donato], Malik, M.I.[Muhammad Imran], Pirlo, G.[Giuseppe], Plamondon, R.[Réjean],
A Perspective Analysis of Handwritten Signature Technology,
Surveys(51), No. 6, February 2019, pp. Article No 117.
DOI Link
Survey, Signatures.
Section, Multiple Entries: 25.4.6.4.3 Signature Recognition, Surveys, Analysis, Comparisons Chapter Contents (Back)
Survey, Signature. Signatures. Signature Recognition.
Plamondon, R.[Réjean], Lorette, G.[Guy],
Automatic Signature Verification and Writer Identification: The State of the Art,
PR(22), No. 2, 1989, pp. 107-131.
Elsevier DOI
Survey, Signature.
Liu, C.L.[Cheng-Lin], Nakashima, K.[Kazuki], Sako, H.[Hiroshi], Fujisawa, H.[Hiromichi],
Handwritten Digit Recognition: Benchmarking of State-of-the-Art Techniques,
PR(36), No. 10, October 2003, pp. 2271-2285.
Elsevier DOI
Survey, Digit Recognition.
Earlier:
Handwritten digit recognition using state-of-the-art techniques,
FHR02(320-325).
IEEE Top Reference.
Jayadevan, R., Kolhe, S.R., Patil, P.M., Pal, U.,
Automatic processing of handwritten bank cheque images: a survey,
IJDAR(15), No. 4, December 2012, pp. 267-296.
WWW Link.
Survey, Checks Processing.
Pal, U., Chaudhuri, B.B.,
Indian script character recognition: a survey,
PR(37), No. 9, September 2004, pp. 1887-1899.
Elsevier DOI
Survey, Indian Script.
Amin, A.,
Off-Line Arabic Character-Recognition: The State of the Art,
PR(31), No. 5, May 1998, pp. 517-530.
Elsevier DOI
Survey, Arabic.
Earlier:
Off-Line Arabic Character Recognition: A Survey,
ICDAR97(596-599).
IEEE DOI
Lorigo, L.M., Govindaraju, V.,
Offline Arabic Handwriting Recognition: A Survey,
PAMI(28), No. 5, May 2006, pp. 712-724.
IEEE DOI
Survey, Arabic.
Earlier:
Segmentation and pre-recognition of Arabic handwriting,
ICDAR05(II: 605-609).
IEEE DOI
Includes recognition rates on test data.
El Abed, H.[Haikal], Kherallah, M.[Monji], Märgner, V.[Volker], Alimi, A.M.[Adel M.],
On-line Arabic handwriting recognition competition: ADAB database and participating systems,
IJDAR(14), No. 1, March 2011, pp. 15-23.
WWW Link.
Earlier: A1, A3, A2, A4:
ICDAR 2009 Online Arabic Handwriting Recognition Competition,
ICDAR09(1388-1392).
IEEE DOI
Evaluation, Arabic Handwriting. Survey, Arabic Handwriting.
Tagougui, N.[Najiba], Kherallah, M.[Monji], Alimi, A.M.[Adel M.],
Online Arabic handwriting recognition: a survey,
IJDAR(16), No. 3, September 2013, pp. 209-226.
Springer DOI
Survey, Arabic Handwriting.
Parvez, M.T.[Mohammad Tanvir], Mahmoud, S.A.[Sabri A.],
Offline arabic handwritten text recognition: A Survey,
Surveys(45), No. 2, February 2013, pp. Article No 23.
DOI Link
Survey, Handwritting, Arabic.
Alginahi, Y.M.[Yasser M.],
A survey on Arabic character segmentation,
IJDAR(16), No. 2, June 2013, pp. 105-126.
WWW Link.
Survey, Arabic Characters.
Al-Helali, B.M.[Baligh M.], Mahmoud, S.A.[Sabri A.],
Arabic Online Handwriting Recognition (AOHR): A Survey,
Surveys(50), No. 3, October 2017, pp. Article No 33.
DOI Link
Survey, Arabic Handwriting. This article comprehensively surveys Arabic Online Handwriting Recognition (AOHR). We address the challenges posed by online handwriting recognition, including ligatures, dots and diacritic problems, online/offline touching of text, and geometric variations.
Ibn-Khedher, M.[Mohamed], Jmila, H.[Houda], El-Yacoubi, M.A.[Mounim A.],
Automatic processing of Historical Arabic Documents: A comprehensive Survey,
PR(100), 2020, pp. 107144.
Elsevier DOI
Survey, Arabic Documents. Historical Arabic Documents, Writer identification, Data retrieval, Text analysis, Text recognition, Survey on Historical Arabic Documents
Section, Multiple Entries: 25.1 Documents and Character Analysis -- Surveys, Comparisons, Evaluations Chapter Contents (Back)
Document Analysis. Application, Document Analysis. Character Recognition. Survey, Document Analysis.
DOCBIB,
1997.
WWW Link. Survey, Document Processing. Survey, OCR. On line bibliography for document processing and character recogbition. Postscript version or search version only. Generally up to 1997.
Andersson, P.L.,
Optical Character Recognition: A Survey,
Datamation(15), No. 7, July 1969, pp. 43-48. Application, Character Recognition. Survey, OCR.
Impedovo, S., Ottaviano, L., Occhinegro, S.,
Optical Character Recognition: A Survey,
PRAI(5), 1991, pp. 1-24. Survey, OCR.
Tang, Y.Y.[Yuan Y.], Lee, S.W.[Seong-Whan], Suen, C.Y.[Ching Y.],
Automatic Document Processing: A Survey,
PR(29), No. 12, December 1996, pp. 1931-1952.
Elsevier DOI
Survey, Document Analysis.
Liang, J.[Jian], Doermann, D.S.[David S.], Li, H.P.[Hui-Ping],
Camera-based analysis of text and documents: a survey,
IJDAR(7), No. 2-3, July 2005, pp. 84-104.
Springer DOI
Survey, Document Analysis.
Fujisawa, H.[Hiromichi],
Forty years of research in character and document recognition: An industrial perspective,
PR(41), No. 8, August 2008, pp. 2435-2446.
Elsevier DOI
Survey, OCR. Survey, Handwriting. OCR; Character recognition; Handwriting recognition; Kanji recognition; Postal address recognition; Robustness design; Information integration; Hypothesis-driven approaches; Digital pen
Ruiz-Parrado, V.[Victoria], Heradio, R.[Ruben], Aranda-Escolastico, E.[Ernesto], Sánchez, Á.[Ángel], Vélez, J.F.[José F.],
A bibliometric analysis of off-line handwritten document analysis literature (1990-2020),
PR(125), 2022, pp. 108513.
Elsevier DOI
Survey, Handwriting Recognition. Automatic document analysis, Off-line handwriting recognition, Writer identification, Signature verification, Bibliometrics, Science mapping
Section, Multiple Entries: 25.2.1 Document Analysis Systems, General, Survey, Evaluation Chapter Contents (Back)
Survey, Document Analysis. Document Analysis.
Nagy, G.[George],
Twenty Years of Document Image Analysis in PAMI,
PAMI(22), No. 1, January 2000, pp. 38-62.
IEEE DOI
Survey, Document Analysis. Part of the 20 year issue. Good survey and discussion of the different aspects of analyzing documents.
Marinai, S.[Simone], Gori, M.[Marco], Soda, G.[Giovanni],
Artificial Neural Networks for Document Analysis and Recognition,
PAMI(27), No. 1, January 2005, pp. 23-35.
IEEE Abstract.
Survey, Document Segmentation. Survey of document segmentation tasks using connectionist approaches. Anaylsis of potential.
Embley, D.W.[David W.], Hurst, M.[Matthew], Lopresti, D.P.[Daniel P.], Nagy, G.[George],
Table-processing paradigms: a research survey,
IJDAR(8), No. 2-3, June 2006, pp. 66-86.
Springer DOI
Survey, Document Segmentation.
Binmakhashen, G.M.[Galal M.], Mahmoud, S.A.[Sabri A.],
Document Layout Analysis: A Comprehensive Survey,
Surveys(52), No. 6, October 2019, pp. xx-yy.
DOI Link
Survey, Document Layout. document structure analysis, Document segmentation, layout analysis, document image retrieval, document image understanding
Nadler, M.[Morton],
Document Segmentation and Coding Techniques,
CVGIP(28), No. 2, November 1984, pp. 240-262.
Elsevier DOI Survey, Page Segmentation.
Pavlidis, T.[Theo], Zhou, J.Y.[Jiang-Ying],
Page Segmentation and Classification,
GMIP(54), No. 6, November 1992, pp. 484-496. Survey, Page Segmentation.
Mao, S.[Song], Kanungo, T.[Tapas],
Empirical Performance Evaluation Methodology and Its Application to Page Segmentation Algorithms,
PAMI(23), No. 3, March 2001, pp. 242-256.
IEEE DOI
Survey, Page Segmentation. Evaluation, Page Segmentation. Created separate test and training data, a computable performance metric, find optimal parameters for different algorithms, evaluate. Compare Voronoi (Kise) (
See also Segmentation of Page Images Using the Area Voronoi Diagram. ); Docstrum (O'Gorman) (
See also Document Spectrum for Page Layout Analysis, The. ); Caere (commercial system) (
See also Caere. ); (these 3 have about the same performance) Are better than ScanSoft (commercial system) (
See also ScanSoft. ); which is better than the older X-Y cut (
See also Prototype Document Image Analysis System for Technical Journals, A. ). Similar conclusion in later analysis:
See also Performance Evaluation and Benchmarking of Six-Page Segmentation Algorithms.
Shafait, F.[Faisal], Keysers, D.[Daniel], Breuel, T.M.[Thomas M.],
Performance Evaluation and Benchmarking of Six-Page Segmentation Algorithms,
PAMI(30), No. 6, June 2008, pp. 941-954.
IEEE DOI
Survey, Page Segmentation. Evaluation, Page Segmentation.
Earlier:
Performance Comparison of Six Algorithms for Page Segmentation,
DAS06(368-379).
Springer DOI
And:
Pixel-Accurate Representation and Evaluation of Page Segmentation in Document Images,
ICPR06(I: 872-875).
IEEE DOI
Also use the dummy program -- no segmentation for a minimum level. X-Y Cut (
See also Prototype Document Image Analysis System for Technical Journals, A. ), Run Length Smearing (
See also Document Analysis System. ), Whitespace Analysis (
See also Two Geometric Algorithms for Layout Analysis. ) and Constrained textline detection. The last two: Docstrum (
See also Document Spectrum for Page Layout Analysis, The. ), Voronoi (
See also Segmentation of Page Images Using the Area Voronoi Diagram. ). are generally the best choice. For similar analysis also see:
See also Empirical Performance Evaluation Methodology and Its Application to Page Segmentation Algorithms.
Kaur, A.[Amandeep], Dhir, R.[Renu], Lehal, G.S.[Gurpreet Singh],
A survey on camera-captured scene text detection and extraction: towards Gurmukhi script,
MultInfoRetr(6), No. 2, June 2017, pp. 115-142.
Springer DOI
Survey, Scene Text.
Chen, X.X.[Xiao-Xue], Jin, L.W.[Lian-Wen], Zhu, Y.Z.[Yuan-Zhi], Luo, C.J.[Can-Jie], Wang, T.W.[Tian-Wei],
Text Recognition in the Wild: A Survey,
Surveys(54), No. 2, March 2021, pp. xx-yy.
DOI Link
Survey, Scene Text. end-to-end systems, deep learning, Scene text recognition
Lienhart, R.,
Video OCR: A Survey and Practitioner's Guide,
VideoMining03(Chapter 6). Survey, OCR.
Jung, K.C.[Kee-Chul], Kim, K.I.[Kwang In], Jain, A.K.[Anil K.],
Text information extraction in images and video: a survey,
PR(37), No. 5, May 2004, pp. 977-997.
Elsevier DOI
Survey, Text Extraction.
Ye, Q.X.[Qi-Xiang], Doermann, D.S.[David S.],
Text Detection and Recognition in Imagery: A Survey,
PAMI(37), No. 7, July 2015, pp. 1480-1500.
IEEE DOI
Survey, Text Detection. Character recognition
Liu, X.[Xiyan], Meng, G.F.[Gao-Feng], Pan, C.H.[Chun-Hong],
Scene text detection and recognition with advances in deep learning: A survey,
IJDAR(22), No. 2, June 2019, pp. 143-162.
WWW Link.
Survey, Scene Text.
Yin, X.C., Zuo, Z.Y., Tian, S., Liu, C.L.,
Text Detection, Tracking and Recognition in Video: A Comprehensive Survey,
IP(25), No. 6, June 2016, pp. 2752-2773.
IEEE DOI
Survey, Scene Text. Data mining
Zanibbi, R.[Richard], Blostein, D.[Dorothea], Cordy, J.R.[James R.],
A survey of table recognition: Models, observations, transformations, and inferences,
IJDAR(7), No. 1, March 2004, pp. 1-16.
Springer DOI
Survey, Document Segmentation.
Doermann, D.S.[David S.],
The Indexing and Retrieval of Document Images: A Survey,
CVIU(70), No. 3, June 1998, pp. 287-298.
DOI Link Survey, Retrieval.
And: UMD--TR3876, February 1998.
WWW Link.
Chiang, Y.Y.[Yao-Yi], Leyk, S.[Stefan], Knoblock, C.A.[Craig A.],
A Survey of Digital Map Processing Techniques,
Surveys(47), No. 1, July 2014, pp. Article No 1.
DOI Link
Survey, Map Processing. This article presents an overview of existing map processing techniques, with the goal of bringing together the past and current research efforts in this interdisciplinary field, to characterize the advances that have been made, and to identify future research directions and opportunities.
Shirai, Y.,
Image Processing for Data Capture,
Computer(15), No. 11, November 1982, pp. 21-34. Survey, Drawing Recognition. Drawing Recognition, Survey. General survey article, but mostly about recognition of drawings.
Zhang, X.L.[Xian-Lin], Li, X.M.[Xue-Ming], Liu, Y.[Yang], Feng, F.X.[Fang-Xiang],
A survey on freehand sketch recognition and retrieval,
IVC(89), 2019, pp. 67-87.
Elsevier DOI
Survey, Sketch Recognition. Touch-screen devices, Freehand sketches, Sketch generation, SBIR, FG-SBIR
Chan, K.F.[Kam-Fai], Yeung, D.Y.[Dit-Yan],
Mathematical Expression Recognition: A Survey,
IJDAR(3), No. 1, 2000, pp. 3-15. Survey, Graphics Recognition.
Rebelo, A., Capela, G., Cardoso, J.S.[Jaime S.],
Optical Recognition of Music Symbols: A Comparative Study,
IJDAR(13), No. 1, March 2010, pp. xx-yy.
Springer DOI
Survey, Music Symbols.
Calvo-Zaragoza, J.[Jorge], Hajic, Jr., J.[Jan], Pacha, A.[Alexander],
Understanding Optical Music Recognition,
Surveys(53), No. 4, July 2020, pp. xx-yy.
DOI Link
Survey, Music. music notation, Optical music recognition, music scores
Ahmed, R.[Rashad], Al-Khatib, W.G.[Wasfi G.], Mahmoud, S.[Sabri],
A Survey on handwritten documents word spotting,
MultInfoRetr(6), No. 1, March 2017, pp. 31-47.
Springer DOI
Survey, Word Spotting.
Ehrmann, M.[Maud], Hamdi, A.[Ahmed], Pontes, E.L.[Elvys Linhares], Romanello, M.[Matteo], Doucet, A.[Antoine],
Named Entity Recognition and Classification in Historical Documents: A Survey,
Surveys(56), No. 2, September 2023, pp. 27.
DOI Link
Survey, Historical Documents. natural language processing, digital humanities, Named entity recognition and classification, historical documents
Section, Multiple Entries: 25.3.1 Watermarks, Survey, Comparisons, Evaluations Chapter Contents (Back)
Survey, Watermark. Watermark.
Rey, C.[Christian], Dugelay, J.L.[Jean-Luc],
A Survey of Watermarking Algorithms for Image Authentication,
JASP(2002), No. 6, June 2002, pp. 613-621.
Survey, Watermark.
Pan, J.S.[Jeng-Shyang], Huang, H.C.[Hsiang-Cheh], Jain, L.C.[Lakhmi C.],
Intelligent Watermarking Techniques,
World ScientificFebruary 2004 ISBN: 978-981-238-757-8 (With CD-Rom) Survey, Watermark. Code, Watermark.
HTML Version. Buy this book: Intelligent Watermarking Techniques (Innovative Intelligence)
Rafidison, M.A.[Maminiaina Alphonse], Rafanantenana, S.H.J.[Sabine Harisoa Jacques], Rakotomihamina, A.H.[Andry Harivony], Toky, R.F.M.[Rajaonarison Faniriharisoa Maxime], Ramafiarisona, H.M.[Hajasoa Malalatiana],
Contribution of neural networks in image steganography, watermarking and encryption,
IET-IPR(17), No. 2, 2023, pp. 463-479.
DOI Link
Survey, Watermarks.
de Vleeschouwer, C., Delaigle, J.F., Macq, B.,
Invisibility and application functionalities in perceptual watermarking an overview,
PIEEE(90), No. 1, January 2002, pp. 64-77.
IEEE DOI
Survey, Watermarks.
Evsutin, O.[Oleg], Dzhanashia, K.[Kristina],
Watermarking schemes for digital images: Robustness overview,
SP:IC(100), 2022, pp. 116523.
Elsevier DOI
Survey, Robust Watermarks. Information security, Data hiding, Digital watermarking, Digital images, Robustness
Section, Multiple Entries: 25.3.10.8 Surveys, Image Hiding, Data Hiding, Steganography Chapter Contents (Back)
Survey, Steganography. Survey, Watermark. Watermark. Watermark, Survey.
Johnson, N.F.[Neil F.], Jajodia, S.[Sushil],
Exploring Steganography: Seeing the Unseen,
Computer(31), No. 2, February 1998, pp. 26-34. Survey, Steganography. Discussion of various available tools for hiding text (and other information) in images.
Petitcolas, F.A.P., Anderson, R.J., Kuhn, M.G.,
Information hiding: A survey,
PIEEE(87), No. 7, July 1999, pp. 1062-1078.
IEEE DOI
Survey, Steganography.
Zielinska, E.[Elzbieta], Mazurczyk, W.[Wojciech], Szczypiorski, K.[Krzysztof],
Trends in Steganography,
CACM(56), No. 3, March 2014, pp. 86-95.
DOI Link
Survey, Steganography. Methods for embedding secret data are more sophisticated than their ancient predecessors, but the basic principles remain unchanged.
Chanu, O.B.[Oinam Bidyapati], Neelima, A.[Arambam],
A survey paper on secret image sharing schemes,
MultInfoRetr(8), No. 4, December 2019, pp. 195-215.
WWW Link.
Survey, Steganography.
Ruan, F.[Feng], Zhang, X.[Xing], Zhu, D.W.[Da-Wei], Xu, Z.Y.[Zhan-Yang], Wan, S.H.[Shao-Hua], Qi, L.Y.[Lian-Yong],
Deep learning for real-time image steganalysis: a survey,
RealTimeIP(17), No. 1, February 2020, pp. 149-160.
Springer DOI
Survey, Steganalysis.
Puteaux, P.[Pauline], Ong, S.Y.[Sim-Ying], Wong, K.[Kok_Sheik], Puech, W.[William],
A survey of reversible data hiding in encrypted images: The first 12 years,
JVCIR(77), 2021, pp. 103085.
Elsevier DOI
Survey, Reversible Data Hiding. Multimedia security, Image encryption, Data hiding, Signal processing in the encrypted domain
Zheng, D.[Dong], Liu, Y.[Yan], Zhao, J.Y.[Ji-Ying], El Saddik, A.[Abdulmotaleb],
A survey of RST invariant image watermarking algorithms,
Surveys(39), No. 2, July 2007, pp. 5.
WWW Link.
Survey, Watermarks.
Lin, E.I., Eskicioglu, A.M., Lagendijk, R.L., Delp, E.J.,
Advances in Digital Video Content Protection,
PIEEE(93), No. 1, January 2005, pp. 171-183.
IEEE DOI
Survey, Authentication.
Mahdian, B.[Babak], Saic, S.[Stanislav],
A bibliography on blind methods for identifying image forgery,
SP:IC(25), No. 6, July 2010, pp. 389-399.
Elsevier DOI
Survey, Image Forensics. Survey, Forgery Detecion. Image forensics; Digital forgery; Image tampering; Blind forgery detection; Multimedia security
Qazi, T., Hayat, K., Khan, S.U., Madani, S.A., Khan, I.A., Kolodziej, J., Li, H., Lin, W., Yow, K.C., Xu, C.Z.,
Survey on blind image forgery detection,
IET-IPR(7), No. 7, October 2013, pp. 660-670.
DOI Link
Survey, Forgery Detection. image forensics
Zheng, L.[Lilei], Zhang, Y.[Ying], Thing, V.L.L.[Vrizlynn L.L.],
A survey on image tampering and its detection in real-world photos,
JVCIR(58), 2019, pp. 380-399.
Elsevier DOI
Survey, Forgery Detection. Image tampering detection, Image forgery detection, Image forensics, Image copy-move detection, Image splicing detection
Qureshi, M.A.[Muhammad Ali], El-Alfy, E.S.M.[El-Sayed M.],
Bibliography of digital image anti-forensics and anti-anti-forensics techniques,
IET-IPR(13), No. 11, 19 September 2019, pp. 1811-1823.
DOI Link
Survey, Forensics.
Du, L.[Ling], Ho, A.T.S.[Anthony T.S.], Cong, R.[Runmin],
Perceptual hashing for image authentication: A survey,
SP:IC(81), 2020, pp. 115713.
Elsevier DOI
Survey, Tamper Detection. Tamper detection, Perceptual image hashing, Content authenticity analysis, Security
Asikuzzaman, M., Pickering, M.R.,
An Overview of Digital Video Watermarking,
CirSysVideo(28), No. 9, September 2018, pp. 2131-2153.
IEEE DOI
Survey, Video Watermarks. Watermarking, Streaming media, Motion pictures, Copyright protection, Internet, multi-view video
Section, Multiple Entries: 25.4.1 Character Recognition Survey, Overview, Evaluations Chapter Contents (Back)
Survey, OCR. OCR. Character Recognition. Evaluation, OCR.
Mantas, J.,
Methodologies in Pattern Recognition and Image Analysis: A Brief Survey,
PR(20), No. 1, 1987, pp. 1-6.
Elsevier DOI
Survey, OCR.
Eckhouse, R., (Editor)
Intelligent Character Recognition,
Computer(23), No. 6, June 1990, pp. 99-103. Survey, OCR Products. Product Survey.
Trier, Ø.D.[Øivind Due], Jain, A.K.[Anil K.], Taxt, T.[Torfinn],
Feature-Extraction Methods for Character-Recognition: A Survey,
PR(29), No. 4, April 1996, pp. 641-662.
Elsevier DOI Survey, OCR.
Wang, P.S.P., and Bunke, H., (Eds.)
Handbook on Optical Character Recognition and Document Image Analysis,
World ScientificPublishing, 1997. Referenced as OCRDIA97
WWW Link. Survey, OCR.
Mirvaziri, H.[Hamid], Javidi, M.M.[Mohammad Masood], Mansouri, N.[Najme],
Handwriting Recognition Algorithm in Different Languages: Survey,
IVIC09(487-497).
Springer DOI
Survey, Handwriting.
Jayadevan, R., Kolhe, S.R., Patil, P.M., Pal, U.,
Offline Recognition of Devanagari Script: A Survey,
SMC-C(41), No. 6, November 2011, pp. 782-796.
IEEE DOI
Survey, OCR. Survey, Devanagari.
Impedovo, S.[Sebastiano],
More than twenty years of advancements on Frontiers in handwriting recognition,
PR(47), No. 3, 2014, pp. 916-928.
Elsevier DOI
Survey, Handwriting Recognition. Handwriting recognition
Nguyen, T.T.H.[Thi Tuyet Hai], Jatowt, A.[Adam], Coustaty, M.[Mickael], Doucet, A.[Antoine],
Survey of Post-OCR Processing Approaches,
Surveys(54), No. 6, July 2021, pp. xx-yy.
DOI Link
Survey, OCR. Survey, Post Processing. error model, language model, OCR merging, machine learning, Post-OCR processing, statistical and neural machine translation
Wang, K.[Kai], Lavoue, G., Denis, F., Baskurt, A.,
A Comprehensive Survey on Three-Dimensional Mesh Watermarking,
MultMed(10), No. 8, December 2008, pp. 1513-1527.
IEEE DOI
Survey, Watermarking.
Bouzegza, M.[Mourad], Belatreche, A.[Ammar], Bouridane, A.[Ahmed], Tounsi, M.[Mohamed],
A comprehensive review of video steganalysis,
IET-IPR(16), No. 13, 2022, pp. 3407-3425.
DOI Link
Survey, Video Steganography.
Sahare, P.[Parul], Dhok, S.B.[Sanjay B.],
Script identification algorithms: a survey,
MultInfoRetr(6), No. 3, September 2017, pp. 211-232.
Springer DOI
Survey, Script.
Wilson, A.[Andrew],
IR Cameras Take Aim at Machine-Vision Applications,
VisSys(17), April 2012, pp. 34-37. Survey, Infrared.
Gade, R.[Rikke], Moeslund, T.B.[Thomas B.],
Thermal cameras and applications: a survey,
MVA(25), No. 1, January 2014, pp. 245-262.
WWW Link.
Survey, Infrared.
Harish Babu, G., Venkatram, N.,
A survey on analysis and implementation of state-of-the-art haze removal techniques,
JVCIR(72), 2020, pp. 102912.
Elsevier DOI
Survey, Haze Removal. Opalescent, Image dehazing, Image restoration, Computational time, Machine learning, Deep learning, Hardware implementation
Gui, J.[Jie], Cong, X.F.[Xiao-Feng], Cao, Y.[Yuan], Ren, W.Q.[Wen-Qi], Zhang, J.[Jun], Zhang, J.[Jing], Cao, J.X.[Jiu-Xin], Tao, D.C.[Da-Cheng],
A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link
Survey, Dehazing. supervised, unsupervised, semi-supervised, Image dehazing, atmospheric scattering model
Su, Z.P.[Zhi-Peng], Zhang, Y.X.[Yi-Xiong], Shi, J.H.[Jiang-Hong], Zhang, X.P.[Xiao-Ping],
A Survey of Single Image Rain Removal Based on Deep Learning,
Surveys(56), No. 4, November 2023, pp. xx-yy.
DOI Link
Survey, Deraining. deep learning, Survey, data-driven, image deraining
Anwar, S.[Saeed], Li, C.Y.[Chong-Yi],
Diving deeper into underwater image enhancement: A survey,
SP:IC(89), 2020, pp. 115978.
Elsevier DOI
Survey, Underwater Images. Underwater image enhancement, Deep learning, Convolutional neural networks (CNNs), Survey
Gonzalez-Sabbagh, S.P.[Salma P.], Robles-Kelly, A.[Antonio],
A Survey on Underwater Computer Vision,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link
Survey, Underwater Vision. underwater image restoration, Underwater computer vision, underwater image formation models, underwater image enhancement
Gijsenij, A.[Arjan], Gevers, T.[Theo], van de Weijer, J.[Joost],
Computational Color Constancy: Survey and Experiments,
IP(20), No. 9, September 2011, pp. 2475-2489.
IEEE DOI
Survey, Color Constancy.
Agarwal, V., Abidi, B.R., Koschan, A.F., Abidi, M.A.,
An Overview of Color Constancy Algorithms,
JPRR(1), No. 1, 2006, pp. 42-54.
PDF File. Survey, Color Constancy.
Farina, A.,
Digital Equalisation in Adaptive Spatial Filtering for Radar Systems: A Survey,
SP(83), No. 1, January 2003, pp. 11-29.
HTML Version.
Survey, Radar.
Eineder, M.[Michael], Moreira, A.[Alberto], Roth, A.[Achim],
Ten Years of TerraSAR-X: Scientific Results,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link
Survey, TerraSAR-X.
Massonnet, D.[Didier], Souyris, J.C.[Jean-Claude],
Synthetic Aperture Radar Imaging,
CRC PressMay, 2008, ISBN: 9780849382390 Survey, SAR.
WWW Link. Buy this book: Synthetic Aperture Radar Imaging (Engineering Sciences: Electrical Engineering)
Zhu, X.X.[Xiao Xiang], Wang, Y.Y.[Yuan-Yuan], Montazeri, S.[Sina], Ge, N.[Nan],
A Review of Ten-Year Advances of Multi-Baseline SAR Interferometry Using TerraSAR-X Data,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link
Survey, SAR Interferometry.
Berizzi, F., Martorella, M., Haywood, B., Mese, E.D., Bruscoli, S.,
A survey on ISAR autofocusing techniques,
ICIP04(I: 9-12).
IEEE DOI
Survey, Radar.
Crosetto, M.[Michele], Monserrat, O.[Oriol], Cuevas-González, M.[María], Devanthéry, N.[Núria], Crippa, B.[Bruno],
Persistent Scatterer Interferometry: A review,
PandRS(115), No. 1, 2016, pp. 78-89.
Elsevier DOI
Survey, Persistent Scatterer. Remote sensing
Section, Multiple Entries: 4.3.1 Morphology - General, Surveys Chapter Contents (Back)
Survey, Morphology. Morphology.
Ahuja, N.,
On Detection and Representation of Multiscale Low-Level Image Structure,
Surveys(27), No. 3, September 1995, pp. 304-306. Survey, Scale-Space.
ter Haar Romeny, B.M.[Bart M.], Florack, L.M.J.[Luc M.J.], Salden, A.H.[Alfons H.], Viergever, M.A.[Max A.],
Higher order differential structure of images,
IVC(12), No. 6, July-August 1994, pp. 317-325.
Elsevier DOI
Survey, Scale Space. Summarize the results of Koenderink and related work.
Lindeberg, T.[Tony],
Scale-Space,
HTML Version. EncycloCSE08 Survey, Scale-Space. Comprehensive overview of the Scale Space in way it has provided the theoretical foundation for more recent work.
Lindeberg, T.[Tony],
Scale-Space Theory in Computer Vision,
Hingham: KluwerAcademic, December 1993. ISBN 0-7923-9418-6.
Springer DOI Survey, Scale-Space. Representation, Scale-Space. Main sections include Basic scale-space theory, Scale-space primal sketch (theory and applications), Scale selection and shape computation. Buy this book: Scale-Space Theory in Computer Vision (The Springer International Series in Engineering and Computer Science)
Croitoru, F.A.[Florinel-Alin], Hondru, V.[Vlad], Ionescu, R.T.[Radu Tudor], Shah, M.[Mubarak],
Diffusion Models in Vision: A Survey,
PAMI(45), No. 9, September 2023, pp. 10850-10869.
IEEE DOI
Survey, Diffusion.
Section, Multiple Entries: 4.5.1 Diffusion Process, Survey, Overview Chapter Contents (Back)
Survey, Diffusion Process. Diffusion Process.
Brady, M.,
Computational Approaches to Image Understanding,
Surveys(14), No. 1, March 1982, pp. 3-71.
And: MIT AI Memo-653, October 1981. Survey, Computational Vision. Computational Vision, Survey. Survey of much of the DARPA research (up to early 81). Concentrates on general processing techniques and ignores applications. Separates the descriptions based on the level of representation being used, especially those that relate to 3-D descriptions. Mostly a review of MIT work with references to other similar and related work, but little unrelated work. Good references on topics covered.
Kragic, D.[Danica], Kyrki, V.[Ville], (Eds.)
Unifying Perspectives in Computational and Robot Vision,
Springer2008, ISBN: 978-0-387-75521-2 Survey, Computational Vision.
WWW Link. Buy this book: Unifying Perspectives in Computational and Robot Vision (Lecture Notes in Electrical Engineering)
Mordohai, P.[Philippos], Medioni, G.[Gérard],
Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning,
Morgan Claypool2006. Synthesis Lectures on Image, Video, and Multimedia Processing
WWW Link. Survey, Tensor Voting. Tensor Voting.
CVonline: Image Physics,
CV-OnlineJuly 2001.
HTML Version. Survey, Image Formation. This includes color and other topics.
CVonline: Sensors and their Properties,
CV-OnlineJuly 2001.
HTML Version. Survey, Sensors. Survey, Cameras.
Kak, A.C., and Albus, J.S.,
Sensors for Intelligent Robots,
HIR84(XX-YY). Survey, Cameras.
And: Purdue-TR-84-2, January 1984. Review of various sensors for robotic applications
Leheny, R.F., McCants, C.E.,
Technologies for Photonic Sensor Systems,
PIEEE(97), No. 6, June 2009, pp. 957-970.
IEEE DOI
Survey, Sensors.
Ye, J.W.[Jin-Wei], Yu, J.Y.[Jing-Yi],
Ray geometry in non-pinhole cameras: A survey,
VC(30), No. 1, January 2014, pp. 93-112.
WWW Link.
Survey, Sensors.
Impedovo, S.[Sebastiano], Modugno, R.[Raffaele], Ferrante, A.[Anna], Stasolla, E.[Erasmo],
New Trends in Digital Scanning Processes,
ICDAR09(1071-1075).
IEEE DOI
Survey, Scanners.
Alamgeer, S.[Sana], Farias, M.C.Q.[Mylène C.Q.],
A survey on visual quality assessment methods for light fields,
SP:IC(110), 2023, pp. 116873.
Elsevier DOI
Survey, Light Field Quality. 4D light field images and videos, Quality assessment methods, Human visual system, Quality metrics
Nagy, G.,
Optical Scanning Digitizers,
Computer(16), No. 5, May 1983, pp. 13-24. Survey, Scanning. Survey of different hardware and techniques.
Lukac, R.[Rastislav],
Single-Sensor Imaging in Consumer Digital Cameras: A Survey of Recent Advances and Future Directions,
RealTimeIP(1), No. 1, October 2006, pp. 45-52.
Springer DOI
Survey, Cameras.
Ishiguro, H.[Hiroshi],
Development of Low-Cost Compact Omnidirectional Vision Sensors,
PV01(23-38).
Survey, Panoramic Sensors. Discussion of the different techniques, spherical mirror, conical mirror, hyperbolic and parabolic mirrors with lenses. The tradeoffs for each system.
Jarvis, R.A.,
A Perspective on Range Finding Techniques for Computer Vision,
PAMI(5), No. 2, March, 1983, pp. 122-139. Survey, Depth Measurement. Survey, Range. Depth Measurement, Survey. A survey of various hardware for use in determining range information. There is some evaluation of the capabilities of the various kinds of systems. The areas included include: Contrived lighting - striped and grid coding, Relative range from occlusion cues, Depth from texture gradient, Range from focusing, Range from stereo, Range from camera motion, Moire range contours, Simple triangulation system, and Time of flight - ultra sonic, laser, streak camera.
Stoykova, E., Alatan, A.A., Benzie, P., Grammalidis, N., Malassiotis, S., Ostermann, J., Piekh, S., Sainov, V., Theobalt, C., Thevar, T., Zabulis, X.,
3-D Time-Varying Scene Capture Technologies: A Survey,
CirSysVideo(17), No. 11, November 2007, pp. 1568-1586.
IEEE DOI
Survey, 3-D Images.
See also Scene Representation Technologies for 3DTV: A Survey.
Horaud, R.[Radu], Hansard, M.[Miles], Evangelidis, G.[Georgios], Ménier, C.[Clément],
An overview of depth cameras and range scanners based on time-of-flight technologies,
MVA(27), No. 7, October 2016, pp. 1005-1020.
Springer DOI
Survey, Depth Cameras.
Section, Multiple Entries: 4.10.1.1 Wavelets, Surveys, Reviews, Overviews, Evaluations, General Chapter Contents (Back)
Wavelets. Representation, Wavelets. Survey, Wavelets.
Chakrabarti, C., Vishwanath, M., Owens, R.M.,
Architectures for Wavelet Transforms: A Survey,
VLSIVideo(14), No. 2, November 1996, pp. 171-192.
Survey, Wavelets.
Sudhakar, R., Karthiga, R., Jayaraman, S.,
Image Compression Using Coding of Wavelet Coefficients: A Survey,
GVIP(05), No. V6, 2005, pp. 25-38
HTML Version. Survey, Wavelets.
CVonline: Color and Reflectance,
CV-OnlineJuly 2001.
WWW Link. Survey, Indexing.
Horn, B.K.P.[Berthold K.P.],
Exact Reproduction of Colored Images,
CVGIP(26), No. 2, May 1984, pp. 135-167.
Elsevier DOI Survey, Color. All the historical references (Newton, and such) on color and human vision. Colored lights, colored dyes.
See also Color Space Analysis of Mutual Illumination.
Gershon, R.[Ron],
Aspects of Perception and Computation in Color Vision,
CVGIP(32), No. 2, November 1985, pp. 244-277.
Elsevier DOI
And: Correction: CVGIP(33), No. 2, February 1986, pp. 259.
Earlier:
Survey on Color: Aspects of Perception and Computation,
RBCV-TR-84-4, July, 1984, Toronto. Survey, Color. Color Perception, Survey.
CVonline: Object, World and Scene Representations,
CV-OnlineJuly 2001.
HTML Version. Survey, Object Representation.
Srihari, S.N.[Sargur N.],
Representation of Three-Dimensional Digital Images,
Surveys(13), No. 4, December 1981, pp. 399-424. Survey, Tomography. Tomography. Data structures. Among other things, compares octree type representation with others.
Barnhill, R.E.,
A Survey of the Representation and Design of Surfaces,
IEEE_CGA(3), No. 7, October 1983, pp. 9-16. Survey, Representation.
Aggarwal, J.K., Davis, L.S., Martin, W.N., Roach, J.W.,
Survey: Representation Methods in Three-Dimensional Objects,
PPR82(377-391). Survey, Representation.
Besl, P.J.[Paul J.], and Jain, R.C.[Ramesh C.],
Three-Dimensional Object Recognition,
Surveys(17), No. 1, March 1985, pp. 75-145. Survey, Representation.
Earlier:
Range Image Understanding,
CVPR85(430-449). Descriptions, Three-Dimensional. Recognize Three-Dimensional Objects. A survey of various techniques.
Fisher, R.B.,
Representing 3D Structures for Visual Recognition,
AIR(1), No. 3, 1987, pp. 183-200. Edinburgh Survey, Representation. Survey of visual model representations
See also SMS: A Suggestive Modelling System for Object Recognition.
Bajcsy, R.,
Signal-to-Symbol Transformation and Vice-Versa: From Fundamental Processes to Representation,
Surveys(27), No. 3, September 1995, pp. 310-313. Survey, Representation.
Loncaric, S.[Sven],
A Survey of Shape Analysis Techniques,
PR(31), No. 8, August 1998, pp. 983-1001.
Elsevier DOI
Survey, Shape.
Lengyel, J.[Jed],
The Convergence of Graphics and Vision,
Computer(31), No. 7, July 1998, pp. 46-53. Survey, Graphics and Vision. Survey of the similarities. Vision generates the models, graphics assume the models. Both have a spectrum of image-based to physical-based techniques, attack from opposite ends.
Campbell, R.J.[Richard J.], Flynn, P.J.[Patrick J.],
A Survey of Free-Form Object Representation and Recognition Techniques,
CVIU(81), No. 2, February 2001, pp. 166-210.
DOI Link
Survey, Object Representation.
Earlier:
Eigenshapes for 3D Object Recognition in Range Data,
CVPR99(II: 505-510).
IEEE DOI Extend appearance based to range data.
de Floriani, L.[Leila], Spagnuolo, M.[Michela], (Eds.)
Shape Analysis and Structuring,
Springer2008, ISBN: 978-3-540-33264-0. Indexed as: ShapeAnalysis08
WWW Link. Survey, Shape Analysis.
Samet, H.[Hanan],
Object-based and image-based object representations,
Surveys(36), No. 2, June 2004, pp. 159-217.
WWW Link.
Survey, Object Representation.
Toriwaki, J.[Junichiro], Yoshida, H.[Hiroyuki],
Fundamentals of Three-dimensional Digital Image Processing,
Springer2009, ISBN: 978-1-84800-172-5
WWW Link. Survey, 3-D. Survey, Representation.
Buy this book: Fundamentals of Three-dimensional Digital Image Processing
Wöhler, C.[Christian],
3D Computer Vision: Efficient Methods and Applications,
Springer2013. ISBN 978-1-4471-4149-5
 
|
WWW Link.
And: Springer2009, ISBN: 978-3-642-01731-5
WWW Link. Survey, 3-D.
Second Edition. 3D Surface reconstruction, pose, high-resolution DEM. Photometric methods, Geometric methods, metrology, photogrammetry.
Tyler, C.W.[Christopher W.], (Ed.)
Computer Vision: From Surfaces to 3D Objects,
CRC PressBoca Raton, FL, December 09, 2010 ISBN: 9781439817124
WWW Link. Buy this book: Computer Vision: From Surfaces to 3D Objects
Survey, 3-D Recognition. Survey, 3-D Description.
Huber, D.F.[Daniel F.],
A new, open standard for 3D imaging data,
SPIE(Newsroom), January 12, 2011.
DOI Link
Survey, 3D Data. E57 Format. The E57 format is a general-purpose, flexible standard for storing data from laser scanners and other 3D-imaging systems.
Edwards, J.,
Three-Dimensional Research Adds New Dimensions,
SPMag(28), No. 3, 2011, pp. 10-13.
IEEE DOI
Survey, 3-D Research. Special Reports
Biasotti, S., Cerri, A., Aono, M., Ben Hamza, A., Garro, V., Giachetti, A., Giorgi, D., Godil, A., Li, C., Sanada, C., Spagnuolo, M., Tatsuma, A., Velasco-Forero, S.,
Retrieval and classification methods for textured 3D models: A comparative study,
VC(32), No. 2, February 2016, pp. 217-241.
Springer DOI
Survey, 3D Models. Comparative study of six methods for the retrieval and classification of textured 3D models.
Potenziani, M.[Marco], Callieri, M.[Marco], Dellepiane, M.[Matteo], Scopigno, R.[Roberto],
Publishing and Consuming 3D Content on the Web: A Survey,
FTCGV(10), No. 4, 2018, pp. 244-333.
DOI Link
Survey, 3D Tools. Survey of the currently available Web3D tools and their applications. Tools for 3D printing, viewing, editing.
Farshian, A.[Anis], Götz, M.[Markus], Cavallaro, G.[Gabriele], Debus, C.[Charlotte], Nießner, M.[Matthias], Benediktsson, J.A.[Jón Atli], Streit, A.[Achim],
Deep-Learning-Based 3-D Surface Reconstruction: A Survey,
PIEEE(111), No. 11, November 2023, pp. 1464-1501.
IEEE DOI
Survey, Surface Reconstruction.
Firman, M.[Michael],
RGBD Datasets: Past, Present and Future,
LS3D16(661-673)
IEEE DOI
Survey, Datasets. reviewing datasets across eight categories: semantics, object pose estimation, camera tracking, scene reconstruction, object tracking, human actions, faces and identification.
Sturm, P.F.[Peter F.],
A Historical Survey of Geometric Computer Vision,
CAIP11(I: 1-8).
Springer DOI
Survey, Geometric.
Cazals, F.[Frédéric], Giesen, J.[Joachim], Yvinec, M.[Mariette],
Delaunay Triangulation Based Surface Reconstruction: A short survey,
INRIARR-5394, 2004.
HTML Version. Survey, Triangulation.
Danelakis, A.[Antonios], Theoharis, T.[Theoharis], Pratikakis, I.E.[Ioannis E.],
3D mesh video retrieval: A survey,
3DTV12(1-4).
IEEE DOI
Survey, Mesh Models. 3D mesh, action retrieval, facial expression retreival.
de Araújo, B.R., Lopes, D.S.[Daniel S.], Jepp, P.[Pauline], Jorge, J.A.[Joaquim A.], Wyvill, B.[Brian],
A Survey on Implicit Surface Polygonization,
Surveys(47), No. 4, July 2015, Article No. 60.
DOI Link
Survey, Polygonization.
Roerdink, J.B.T.M.[Jos B. T. M.],
Morphological Pyramids in Multiresolution MIP Rendering of Large Volume Data: Survey and New Results,
JMIV(22), No. 2-3, May 2005, pp. 143-157.
Springer DOI
Survey, Rendering.
Earlier:
A New Class of Morphological Pyramids for Multiresolution Image Analysis,
WTRCV02(165-175).
Birajdar, G.K.[Gajanan K.], Patil, M.D.[Mukesh D.],
A Systematic Survey on Photorealistic Computer Graphic and Photographic Image Discrimination,
IJIG(23), No. 4 2023, pp. 2350037.
DOI Link
Survey, Computer Graphics.
Peng, J.L.[Jing-Liang], Kim, C.S.[Chang-Su], Kuo, C.C.J.[C.C. Jay],
Technologies for 3D mesh compression: A survey,
JVCIR(16), No. 6, December 2005, pp. 688-733.
Elsevier DOI
Survey, Mesh Compression. 3D mesh compression; Single-rate mesh coding; Progressive mesh coding; MPEG-4
Maglo, A.[Adrien], Lavoué, G.[Guillaume], Dupont, F.[Florent], Hudelot, C.[Céline],
3D Mesh Compression: Survey, Comparisons, and Emerging Trends,
Surveys(47), No. 3, April 2015, pp. Article No 44.
DOI Link
Survey, Mesh Compression. 3D meshes are commonly used to represent virtual surface and volumes. However, their raw data representations take a large amount of space. Hence, 3D mesh compression has been an active research topic since the mid 1990s.
Deng, Y.D.[Yang-Dong], Ni, Y.F.[Yu-Fei], Li, Z.H.[Zong-Hui], Mu, S.[Shuai], Zhang, W.J.[Wen-Jun],
Toward Real-Time Ray Tracing: A Survey on Hardware Acceleration and Microarchitecture Techniques,
Surveys(50), No. 4, November 2017, pp. Article No 58.
DOI Link
Survey, Ray Tracing. technology for real-time graphics.
Nazir, A.[Ambreen], Rao, Y.[Yuan], Wu, L.[Lianwei], Sun, L.[Ling],
Issues and Challenges of Aspect-based Sentiment Analysis: A Comprehensive Survey,
AffCom(13), No. 2, April 2022, pp. 845-863.
IEEE DOI
Survey, Sentiment Analysis. Sentiment analysis, Social networking (online), Data mining, Machine learning, Task analysis, Tools, Sun, Aspect, social media
Abdullah, T.[Tariq], Ahmet, A.[Ahmed],
Deep Learning in Sentiment Analysis: Recent Architectures,
Surveys(55), No. 8, December 2022, pp. xx-yy.
DOI Link
Survey, Sentiment Analysis. cross-domain sentiment analysis, transfer learning, cross-lingual sentiment analysis, sentiment analysis
Park, E.H.[Eun Hee], Storey, V.C.[Veda C.],
Emotion Ontology Studies: A Framework for Expressing Feelings Digitally and Its Application to Sentiment Analysis,
Surveys(55), No. 9, January 2023, pp. xx-yy.
DOI Link
Survey, Sentiment Analysis. sentiment analysis, dimensional emotion ontology, affect, discrete emotion ontology, Framework of Emotion Ontologies, emotion
Das, R.K.[Ring-Ki], Singh, T.D.[Thoudam Doren],
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link
Survey, Sentiment. audio sentiment analysis, image sentiment analysis, text sentiment analysis, Multimodal sentiment analysis, transfer learning
Ibrohim, M.O.[Muhammad Okky], Bosco, C.[Cristina], Basile, V.[Valerio],
Sentiment Analysis for the Natural Environment: A Systematic Review,
Surveys(56), No. 4, November 2023, pp. xx-yy.
DOI Link
Survey, Sentiment. sentiment analysis, Natural environment, data-driven policy, natural language processing (NLP), systematic review
Morvan, J.M.[Jean-Marie],
Generalized Curvatures,
Springer2008, ISBN: 978-3-540-73791-9
WWW Link. Survey, Curvature. Buy this book: Generalized Curvatures (Geometry and Computing)
Yu, T.[Ting], Lin, X.J.[Xiao-Jun], Wang, S.H.[Shu-Hui], Sheng, W.G.[Wei-Guo], Huang, Q.M.[Qing-Ming], Yu, J.[Jun],
A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing Objects in 3D Scenes,
CirSysVideo(34), No. 3, March 2024, pp. 1322-1338.
IEEE DOI
Survey, Captioning. Survey, Object Localization. Task analysis, Visualization, Point cloud compression, Grounding, Surveys, Solid modeling, 3D dense captioning, 3D point cloud
Bolle, R.M., and Vemuri, B.C.,
On Three-Dimensional Surface Reconstruction Methods,
PAMI(13), No. 1, January 1991, pp. 1-13.
IEEE DOI Survey, Surface Reconstruction. Surface Reconstruction, Survey. Describes a method, but no pictures.
Boult, T.E.[Terrance E.], and Kender, J.R.,
Visual Surface Reconstruction Using Sparse Depth Data,
CVPR86(68-76).
Earlier:
On Surface Reconstruction Using Sparse Depth Data,
DARPA85(197-208). Survey, Surface Reconstruction. Surface Reconstruction, Survey. General survey of the techniques, the paper discusses the implementation using splines. Gets better when the data is sparse, dense data takes too long.
Petitjean, S.[Sylvain],
A survey of methods for recovering quadrics in triangle meshes,
Surveys(34), No. 2, February 2002, pp. 211-262. Survey, Mesh. Survey, Triangulation.
Section, Multiple Entries: 11.3.12 Surveys, Overviews, Evaluations and Analysis of 3-D Reconstructions Chapter Contents (Back)
Survey, Reconstruction. Reconstruction, 3-D. Reconstruction.
Goshtasby, A.A.[A. Ardeshir],
Three-dimensional model construction from multiview range images: Survey with new results,
PR(31), No. 11, November 1998, pp. 1705-1714.
Elsevier DOI Survey, Reconstruction.
Krispel, U.[Ulrich], Schinko, C.[Christoph], Ullrich, T.[Torsten],
A Survey of Algorithmic Shapes,
RS(7), No. 10, 2015, pp. 12763.
DOI Link
Survey, Shape Description.
Bhanu, B.[Bir], (Ed.)
CAD-Based Robot Vision,
Computer(20), No. 8, August, 1987. Special issue. Survey, CAD. CAD, Survey.
Requicha, A.A.G.,
Representations for Rigid Solids: Theory, Methods and Systems,
Surveys(12), No. 4, December 1980, pp. 437-464. Survey, Representation.
Ji, Q., Marefat, M.M.,
Machine Interpretation of CAD Data for Manufacturing Applications,
Surveys(29), No. 3, September 1997, pp. 264-311.
Survey, CAD.
Section, Multiple Entries: 11.8.1 Deformable Models, General, Overview Chapter Contents (Back)
Deformable Models. Nonrigid Models. Survey, Deformable Models.
Metaxas, D.N.[Dimitris N.],
Physics-Based Deformable Models Applications to Computer Vision, Graphics and Medical Imaging,
KluwerNovember 1996, ISBN 0-7923-9840-8.
WWW Link. Survey, Deformable Models.
Montagnat, J., Delingette, H., Ayache, N.,
A review of deformable surfaces: topology, geometry and deformation,
IVC(19), No. 14, December 2001, pp. 1023-1040.
Elsevier DOI
Survey, Deformable Models.
Bronstein, A.M.[Alexander M.], Bronstein, M.M.[Michael M.], Kimmel, R.[Ron],
Numerical Geometry of Non-Rigid Shapes,
Springer2008, ISBN: 978-0-387-73300-5.
WWW Link. Survey, Nonrigid Shape. Buy this book: Numerical Geometry of Non-Rigid Shapes (Monographs in Computer Science)
Lian, Z.H.[Zhou-Hui], Godil, A.[Afzal], Bustos, B.[Benjamin], Daoudi, M.[Mohamed], Hermans, J.[Jeroen], Kawamura, S.[Shun], Kurita, Y.[Yukinori], Lavoué, G.[Guillaume], Nguyen, H.V.[Hien Van], Ohbuchi, R.[Ryutarou], Ohkita, Y.[Yuki], Ohishi, Y.[Yuya], Porikli, F.M.[Fatih M.], Reuter, M.[Martin], Sipiran, I.[Ivan], Smeets, D.[Dirk], Suetens, P.[Paul], Tabia, H.[Hedi], Vandermeulen, D.[Dirk],
A comparison of methods for non-rigid 3D shape retrieval,
PR(46), No. 1, January 2013, pp. 449-461.
Elsevier DOI
Earlier:
SHREC'11 Track: Shape Retrieval On Non-Rigid 3d Watertight Meshes,
3DOR11(79-88)
DOI Link
Survey, Non-Rigid Shape. 3D shape retrieval; Non-rigid; Benchmark
McInerney, T., Terzopoulos, D.,
Deformable Models in Medical Image Analysis: A Survey,
MIA(1), No. 2, 1996, pp. 91-108. Survey, Deformable Models.
Holden, M.,
A Review of Geometric Transformations for Nonrigid Body Registration,
MedImg(27), No. 1, January 2008, pp. 111-128.
IEEE DOI
Survey, Deformable Registration.
Tavakoli, V.[Vahid], Amini, A.A.[Amir A.],
A survey of shaped-based registration and segmentation techniques for cardiac images,
CVIU(117), No. 9, 2013, pp. 966-989.
Elsevier DOI
Survey, Cardiac Segmentation. Cardiac CT
Liu, Y.[Yanbin], Dwivedi, G.[Girish], Boussaid, F.[Farid], Bennamoun, M.[Mohammed],
3D Brain and Heart Volume Generative Models: A Survey,
Surveys(56), No. 6, January 2024, pp. xx-yy.
DOI Link
Survey, 3D Models. Generative models, three-dimensional, medical images, brain and heart
Chen, H.H.[Homer H.], Huang, T.S.[Thomas S.],
A Survey of Construction and Manipulation of Octrees,
CVGIP(43), No. 3, September 1988, pp. 409-431.
Elsevier DOI Survey, Octree. Octree. A good source for the early history and its relation to graphics where most of the early work was centered.
Zhao, F.[Feng],
Machine Recognition as representation and search: A Survey,
PRAI(5), 1991, pp. 715-747. Survey, Representation.
Earlier:
Machine Recognition as Representation and Search,
MIT AI Memo-1189, December 1989.
WWW Link.
Kemper, A., and Wallrath, M.,
An Analysis of Geometric Modeling in Database Systems,
Surveys(19), No. 1, March 1987, pp. 47-91. Survey, Representation.
Blicher, A.P.[A. Peter],
Edge Detection and Geometric Methods in Computer Vision,
Ph.D.Thesis, December 1984, Mathematics Department, UCB Survey, Edge Detection.
And: Stanford University STAN-CS-85-1041,
And: Stanford AI-AIM-352, February 1985. Mainly applications of differential topology, to problems in edge detection, edge grouping, image matching, stereo vision, use of color. Survey of the edge detection literature through Canny.
Boehm, W.[Wolfgang],
On Cubics: a Survey,
CGIP(19), No. 3, July 1982, pp. 201-226.
Elsevier DOI Survey, Cubics.
Lee, D.T., Preparata, F.P.,
Computational Geometry: A Survey,
TC(33), 1984, pp. 1072-1101. Survey, Computational Geometry.
Heyden, A.[Anders], and Pollefeys, M.[Marc],
Multiple View Geometry,
ETCV04(Chapter 3). Survey, Projective Geometry.
Kang, S.B.[Sing Bing], Li, Y.[Yin], Tong, X.[Xin], Shum, H.Y.[Heung-Yeung],
Image-Based Rendering,
FTCGV(2), Issue 3, 2006, pp. 173-258.
DOI Link
Published March 2007. Survey, Image Based Rendering.
Scharstein, D.,
View Synthesis Using Stereo Vision,
Springer-Verlag1999. LNCS1583, 1999. ISBN 3-540-66159-X.
Springer DOI Survey, Stereo. Survey, Image Based Rendering. Image based rendering and stereo.
CVonline: Occlusion Understanding and Recovery,
CV-Online2002.
HTML Version. Survey, Surface Reconstrutcion.
Ao, J.Y.[Jia-Yang], Ke, Q.H.[Qiu-Hong], Ehinger, K.A.[Krista A.],
Image amodal completion: A survey,
CVIU(229), 2023, pp. 103661.
Elsevier DOI
Survey, Image Completion. Artificial intelligence, Machine vision and scene understanding, Amodal completion
Wang, J.[Jue], Cohen, M.F.[Michael F.],
Image and Video Matting: A Survey,
FTCGV(3), Issue 2, 2007, pp. 91-175.
DOI Link
Survey, Matting. Published May 2008.
Lepcha, D.C.[Dawa Chyophel], Goyal, B.[Bhawna], Dogra, A.[Ayush],
Image Matting: A Comprehensive Survey on Techniques, Comparative Analysis, Applications and Future Scope,
IJIG(23), No. 1 2023, pp. 2350011.
DOI Link
Survey, Image Matting.
Guillemot, C., Le Meur, O.,
Image Inpainting: Overview and Recent Advances,
SPMag(31), No. 1, January 2014, pp. 127-144.
IEEE DOI
Survey, Inpainting. image restoration
Jam, J.[Jireh], Kendrick, C.[Connah], Walker, K.[Kevin], Drouard, V.[Vincent], Hsu, J.G.S.[Jison Gee-Sern], Yap, M.H.[Moi Hoon],
A comprehensive review of past and present image inpainting methods,
CVIU(203), 2021, pp. 103147.
Elsevier DOI
Survey, Inpainting. Image inpainting, Restoration, Texture synthesis, Convolutional neural network, Generative adversarial networks
Xiang, H.Y.[Han-Yu], Zou, Q.[Qin], Nawaz, M.A.[Muhammad Ali], Huang, X.F.[Xian-Feng], Zhang, F.[Fan], Yu, H.K.[Hong-Kai],
Deep learning for image inpainting: A survey,
PR(134), 2023, pp. 109046.
Elsevier DOI
Survey, Inpainting. Image inpainting, Image restoration, Generative adversarial network, Convolutional neural network
Section, Multiple Entries: 11.14.3.12 Image Based Rendering, IBR, Surveys, Reviews, Evaluations Chapter Contents (Back)
Survey, Image Based Rendering. Graphics.
Girod, B.[Bernd], Greiner, G.[Günther], Niemann, H.[Heinrich],
Principles of 3D Image Analysis and Synthesis,
KluwerMay 2000, ISBN 0-7923-7850-4 Indexed as: 3DIAS00
WWW Link. Survey, Image Based Rendering. Buy this book: Principles of 3D Image Analysis and Synthesis (The Springer International Series in Engineering and Computer Science)
Shum, H.Y.[Heung-Yeung], Kang, S.B.[Sing Bing], Chan, S.C.[Shing-Chow],
Survey of image-based representations and compression techniques,
CirSysVideo(13), No. 11, November 2003, pp. 1020-1037.
IEEE Abstract.
Survey, Image Based Rendering.
Zhang, C.[Cha], Chen, T.H.[Tsu-Han],
A Survey on Image-Based Rendering: Representation, Sampling and Compression,
SP:IC(19), No. 1, January 2004, pp. 1-28.
Elsevier DOI
Survey, Image Based Rendering.
Zhang, C.[Cha], Chen, T.H.[Tsu-Han],
Light Field Sampling,
Morgan Claypool2007. Synthesis Lectures on Image, Video, and Multimedia Processing
DOI Link Survey, Image Based Rendering.
Alatan, A.A.[A. Aydin], Yemez, Y.[Yucel], Gudukbay, U.[Ugur], Zabulis, X.[Xenophon], Muller, K.[Karsten], Erdem, C.E.[Cigdem Eroglu], Weigel, C.[Christian], Smolic, A.[Aljoscha],
Scene Representation Technologies for 3DTV: A Survey,
CirSysVideo(17), No. 11, November 2007, pp. 1587-1605.
IEEE DOI
Survey, 3-D Representation.
See also 3-D Time-Varying Scene Capture Technologies: A Survey.
Kang, S.B.[Sing Bing], Li, Y.[Yin], Tong, X.[Xin], Shum, H.Y.[Heung-Yeung],
Image Based Rendering,
World ScientificSingapore, 2007. ISBN: 978-1-60198-018-2 Survey, Image Based Rendering. Buy this book: Image-Based Rendering (Foundations and Trends(R) in Computer Graphics and Vision(R))
Shum, H.Y.[Heung-Yeung], Chan, S.C.[Shing-Chow], Kang, S.B.[Sing Bing],
Image-Based Rendering,
Springer2007, ISBN: 978-0-387-21113-8.
WWW Link. Survey, Image Based Rendering. Part I: Basic Concepts: Direct Image Representations. Representations with Explicit Geometry. Rendering Dynamic Scenes. Representation and Rendering Issues. Part II: Sampling: Plenoptic Sampling. A Geometric Analysis of Light Field Rendering. Sampling in Dual Space. Bibliography. Part III: Compression: Data Compression Techniques. Image and Video Compression. Compression of Static Image-based Representations. Compression of Dynamic Image-based Representations. Part IV: Systems and Applications: Rendering by Manifold Hopping. Large Environment Rendering Using Plenoptic Primitives. Pop-Up Light Field: An Interactive Image-based Modeling and Rendering System. Feature-based Light Field Morphing
Yu, Z.W.[Zhi-Wen], Nakamura, Y.[Yuichi],
Smart meeting systems: A survey of state-of-the-art and open issues,
Surveys(42), No. 2, February 2010, pp. xx-yy.
WWW Link.
Survey, Video Conference.
Meesters, L.M.J., IJsselsteijn, W.A., Seuntiens, P.J.H.,
A survey of perceptual evaluations and requirements of three-dimensional TV,
CirSysVideo(14), No. 3, March 2004, pp. 381-391.
IEEE Abstract.
Survey, Display.
Apostolopoulos, J.G., Chou, P.A., Culbertson, B., Kalker, T., Trott, M.D., Wee, S.,
The Road to Immersive Communication,
PIEEE(100), No. 4, April 2012, pp. 974-990.
IEEE DOI
Survey, Immersion.
Chan, L., Naghdy, F., Stirling, D.,
Application of Adaptive Controllers in Teleoperation Systems: A Survey,
HMS(44), No. 3, June 2014, pp. 337-352.
IEEE DOI
Survey, Teleoperation. Adaptation models
Salam, H.[Hanan], Séguier, R.[Renaud],
A survey on face modeling: building a bridge between face analysis and synthesis,
VC(34), No. 2, February 2018, pp. 289-319.
Springer DOI
Survey, Face Modeling.
Toshpulatov, M.[Mukhiddin], Lee, W.[Wookey], Lee, S.[Suan],
Generative adversarial networks and their application to 3D face generation: A survey,
IVC(108), 2021, pp. 104119.
Elsevier DOI
Survey, Face Synthesis. Survey, GAN. Generative adversarial networks, 3 face generation, Generator, Discriminator, Deep neural network, Deep learning
Abrantes, G.A., Pereira, F.,
MPEG-4 Facial Animation Technology: Survey, Implementation, and Results,
CirSysVideo(9), No. 2, March 1999, pp. 290.
IEEE Top Reference. Survey, Face Animation.
Deng, Z.G.[Zhi-Gang], Neumann, U.[Ulrich], (Eds.)
Data-Driven 3D Facial Animation,
Springer2008, ISBN: 978-1-84628-906-4.
WWW Link. Survey, Face Animation. Collection of papers on modeling and animation. Buy this book: Data-Driven 3D Facial Animation
Yu, P.[Peipeng], Xia, Z.H.[Zhi-Hua], Fei, J.W.[Jian-Wei], Lu, Y.J.[Yu-Jiang],
A Survey on Deepfake Video Detection,
IET-Bio(10), No. 6, 2021, pp. 607-624.
DOI Link
Survey, Deepfakes.
Mishima, K.[Ken], Yamana, H.[Hayato],
A Survey on Explainable Fake News Detection,
IEICE(E105-D), No. 7, July 2022, pp. 1249-1257.
WWW Link.
Survey, Fake News.
Kshetri, N.[Nir],
The Economics of Deepfakes,
Computer(56), No. 8, August 2023, pp. 89-94.
IEEE DOI
Survey, Deepfakes.
Liu, C.[Chang], Yu, H.[Han],
AI-Empowered Persuasive Video Generation: A Survey,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link
Survey, Video Generation. storyline generation, video generation, Artificial intelligence
Mirsky, Y.[Yisroel], Lee, W.[Wenke],
The Creation and Detection of Deepfakes: A Survey,
Surveys(54), No. 1, January 2021, pp. xx-yy.
DOI Link
Survey, Deepfakes. impersonation, generative AI, social engineering, face swap, replacement, deep fake, Deepfake, reenactment
Testa, R.L.[Rafael Luiz], Corrêa, C.G.[Cléber Gimenez], Machado-Lima, A.[Ariane], Nunes, F.L.S.[Fátima L. S.],
Synthesis of Facial Expressions in Photographs: Characteristics, Approaches, and Challenges,
Surveys(51), No. 6, February 2019, pp. Article No 124.
DOI Link
Survey, Face Synthesis.
Davis, L.S.[Larry S.],
A Survey of Edge Detection Techniques,
CGIP(4), No. 3, September 1975, pp. 248-270.
Elsevier DOI Survey, Edge Detection. Edge Detection, Survey. Mathematical discussion. Even this early, there are enough to survey.
Spontón, H.[Haldo], Cardelino, J.[Juan],
A Review of Classic Edge Detectors,
Image Processing On Line(5), 2015, pp. 90-123.
DOI Link
Survey, Edge Detectors. A discussion of classic edge detectors, how they are similar, what the generate, etc.
Basu, M.,
Gaussian-based edge-detection methods: A Survey,
SMC-C(32), No. 3, August 2002, pp. 252-260.
IEEE Top Reference.
Survey, Edge Detection.
Papari, G.[Giuseppe], Petkov, N.[Nicolai],
Edge and line oriented contour detection: State of the art,
IVC(29), No. 2-3, February 2011, pp. 79-103.
Elsevier DOI
Survey, Edge Detection. Contour detection; Preprocessing; Local pattern analysis; Contour salience; Gestalt grouping; Closure; Scale-space; Performance evaluation
Ehret, T.[Thibaud], Morel, J.M.[Jean-Michel],
Line Segment Detection: a Review of the 2022 State of the Art,
IPOL(14), 2024, pp. 41-63.
DOI Link
Survey, Line Segments Detection.
Nielson, G.M.[Gregory M.],
Tools for Triangulations and Tetrahedrizations and Constructing Functions defined over Them,
Scientific Visualization1997, CS-PressGood survey of techniques. Survey, Triangulation.
Tombre, K., Tabbone, S.A.,
Vectorization in Graphics Recognition: To Thin or Not to Thin,
ICPR00(Vol II: 91-96).
IEEE DOI
Survey, Vectorication.
Ali, D.[Dashti], Asaad, A.[Aras], Jimenez, M.J.[Maria-Jose], Nanda, V.[Vidit], Paluzo-Hidalgo, E.[Eduardo], Soriano-Trigueros, M.[Manuel],
A Survey of Vectorization Methods in Topological Data Analysis,
PAMI(45), No. 12, December 2023, pp. 14069-14080.
IEEE DOI
Survey, Vectorization.
Freeman, H.,
Computer Processing of Line Drawing Images,
Surveys(6), No. 1, March 1974, pp. 57-97.
WWW Link. Survey, Chain Code. Chain Codes, Survey. The complete basic paper for chain codes and others.
See also Comparative Analysis of Line-Drawing Modeling Schemes.
Chernov, N.[Nikolai],
Circular and Linear Regression: Fitting Circles and Lines by Least Squares,
CRC PressBoca Raton, FL, June 22, 2010 ISBN: 9781439835906.
WWW Link. Buy this book: Circular and Linear Regression: Fitting Circles and Lines by Least Squares (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
Code, Image Processing, Matlab.
Survey, Circle Fitting.
CVOnline: Hough Transform,
CV-Online2006.
WWW Link. Survey, Hough.
Illingworth, J., Kittler, J.V.,
A Survey of the Hough Transform,
CVGIP(44), No. 1, October 1988, pp. 87-116.
Elsevier DOI Survey, Hough. Hough. Hough, Survey. This should be the basic standard reference in the future.
Leavers, V.F.,
Survey: Which Hough Transform?,
CVGIP(58), No. 2, September 1993, pp. 250-264.
DOI Link Survey, Hough.
Herout, A.[Adam], Dubská, M.[Markéta], Havel, J.[Jirí],
Real-Time Detection of Lines and Grids: By PClines and Other Approaches,
Springer2013. ISBN 978-1-4471-4413-7
 
|
WWW Link.
Survey, Hough.
Mukhopadhyay, P.[Priyanka], Chaudhuri, B.B.[Bidyut B.],
A survey of Hough Transform,
PR(48), No. 3, 2015, pp. 993-1010.
Elsevier DOI
Survey, Hough Transform. Hough Transform. (HT)
Iannino, A., Shapiro, S.D.,
A Survey of the Hough Transform and Its Extensions for Curve Detection,
PRIP78(32-38). Survey, Hough.
CVonline: Image Transformations and Filters,
CV-OnlineJuly 2001.
HTML Version. Survey, Filters. Survey, Transforms.
Milanfar, P.,
A Tour of Modern Image Filtering: New Insights and Methods, Both Practical and Theoretical,
SPMag(30), No. 1, 2012, pp. 106-128.
IEEE DOI
Survey, Filters.
Welch, G.[Greg], and Bishop, G.[Gary],
An Introduction to the Kalman Filter,
TR95-041, University of North Carolina at Chapel Hill, Department of Computer Science, 1995.
WWW Link. Survey, Kalman Filter. Code, Kalman Filter. Tutorial on Kalman filter. All you want to know.
Faragher, R.,
Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation,
SPMag(29), No. 5, 2012, pp. 128-132.
IEEE DOI
Survey, Kalman Filter.
O'Grady, P.D.[Paul D.], Pearlmutter, B.A.[Barak A.], Rickard, S.T.[Scott T.],
Survey of sparse and non-sparse methods in source separation,
IJIST(15), No. 1, 2005, pp. 18-33.
DOI Link
Survey, Source Separation.
Fan, J.L.[Jin-Long], Zhang, J.[Jing], Maybank, S.J.[Stephen J.], Tao, D.C.[Da-Cheng],
Wide-Angle Image Rectification: A Survey,
IJCV(130), No. 3, March 2022, pp. 747-776.
Springer DOI
Survey, Rectification.
Section, Multiple Entries: 5.3.1 Image Restoration -- General, Survey, Evaluations Chapter Contents (Back)
Restoration. Survey, Restoration.
See also Image Quality Evaluation, Visual Quality, Quality Assessment, and Imaging Models.
See also Non-Local Means for Denoising.
Sezan, M.I., Tekalp, A.M.,
Survey of recent developments in digital image restoration,
OptEng(29), No. 5, May 1990, pp. 393-404. Survey, Restoration. In the Special issue.
Shao, L., Yan, R., Li, X., Liu, Y.,
From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms,
Cyber(44), No. 7, July 2014, pp. 1001-1013.
IEEE DOI
Survey, Denoising. Dictionaries
Olsen, S.I.[Søren I.],
Estimation of Noise in Images: An Evaluation,
GMIP(55), No. 4, July 1993, pp. 319-323.
DOI Link Survey, Noise Estimation.
Lebrun, M., Colom, M., Baudes, A., Morel, J.M.,
Secrets of Image Denoising cuisine,
Acta Numerica(21), 2012, pp. 475-576.
DOI Link
Survey, Noise Estimation.
Elad, M.[Michael], Kawar, B.[Bahjat], Vaksman, G.[Gregory],
Image Denoising: The Deep Learning Revolution and Beyond: A Survey Paper,
SIIMS(16), No. 3, 2023, pp. 1594-1654.
DOI Link
Survey, Denoising.
Mafi, M.[Mehdi], Izquierdo, W.[Walter], Cabrerizo, M.[Mercedes], Barreto, A.[Armando], Andrian, J.[Jean], Rishe, N.D.[Naphtali David], Adjouadi, M.[Malek],
Survey on mixed impulse and Gaussian denoising filters,
IET-IPR(14), No. 16, 19 December 2020, pp. 4027-4038.
DOI Link
Survey, Noise Filter.
Soundararajan, R.[Rajiv], Bovik, A.C.[Alan C.],
Survey of information theory in visual quality assessment,
SIViP(7), No. 3, May 2013, pp. 391-401.
WWW Link.
Survey, Visual Quality.
Nehab, D.[Diego], Hoppe, H.[Hugues], Pedersen, M.[Marius], Hardeberg, J.Y.[Jon Yngve],
Full-Reference Image Quality Metrics: Classification and Evaluation,
FTCGV(7), Issue 1, 2011, pp. 1-80.
DOI Link
Survey, Image Quality. Published March 2012.
You, J.Y.[Jun-Yong], Reiter, U.[Ulrich], Hannuksela, M.M.[Miska M.], Gabbouj, M.[Moncef], Perkis, A.[Andrew],
Perceptual-based quality assessment for audio-visual services: A survey,
SP:IC(25), No. 7, August 2010, pp. 482-501.
Elsevier DOI
Survey, Video Quality. Objective quality metric; Subjective quality assessment; HVS; Psychophysical approach; Engineering approach; Perception; Alignment; PEAQ; Semantic importance
Alaei, A.[Alireza], Bui, V.[Vinh], Doermann, D.[David], Pal, U.[Umapada],
Document Image Quality Assessment: A Survey,
Surveys(56), No. 2, September 2023, pp. 29.
DOI Link
Survey, Document Quality. image quality assessment, Document image quality, document image readability
Wang, Z.[Zhou], Bovik, A.C.[Alan C.],
Modern Image Quality Assessment,
Morgan Claypool2006. Synthesis Lectures on Image, Video, and Multimedia Processing Survey, Image Quality.
WWW Link.
Lelewer, D.A., Hirschberg, D.S.,
Data Compression,
Surveys(19), No. 3, September 1987, pp. 261-296. Survey, Compression.
Salomon, D.[David], Motta, G.[Giovanni],
Handbook of Data Compression,
Springer2010, ISBN: 978-1-84882-902-2
WWW Link. Survey, Image Compression. Survey, Video Compression. Compression. Buy this book: Handbook of Data Compression
Section, Multiple Entries: 5.4.1.1 Image Compression, Coding, Surveys, Evaluations and Overviews Chapter Contents (Back)
Image Compression. Image Coding. Evaluation, Image Coding. Survey, Compression. Survey, Coding.
Reid, M.M., Millar, R.J., Black, N.D.,
Second-Generation Image-Coding: An Overview,
Surveys(29), No. 1, March 1997, pp. 3-29.
Survey, Compression. Given the 1985 paper above, maybe it should be third-generation. Describes properties of the human visual system that make coding work. Multiscale and pyramidal techniques, directional filters, coding using patterns, techniques using segmentation, contour-based approaches.
Brittain, J.E.,
Scanning The Past,
PIEEE(83), No. 2, February 1995, pp. 338-338. Survey, Compression.
Clarke, R.J.[Roger J.],
Image and video compression: A survey,
IJIST(10), No. 1, 1999, pp. 20-32. Survey, Compression.
Jiang, J.,
Image compression with neural networks: A survey,
SP:IC(14), No. 9, July 1999, pp. 737-760.
Elsevier DOI Survey, Compression.
Yang, Y.[Yibo], Mandt, S.[Stephan], Theis, L.[Lucas],
An Introduction to Neural Data Compression,
FTCGV(15), No. 2, 2023, pp. 113-200.
DOI Link
Survey, Compression.
Section, Multiple Entries: 5.4.3.1 Vector Quantization Survey and General Chapter Contents (Back)
Compression. Vector Quantization. Survey, Compression. Survey, Vector Quantization.
Cosman, P.C., Gray, R.M., Vetterli, M.,
Vector Quantization of Image Subbands: A Survey,
IP(5), No. 2, February 1996, pp. 202-225.
IEEE DOI Survey, Vector Quantization.
Cerda-Company, X.[Xim], Parraga, C.A.[C. Alejandro], Otazu, X.[Xavier],
Which tone-mapping operator is the best? A comparative study of perceptual quality,
JOSA-A(35), No. 4, April 2018, pp. 626-638.
DOI Link
Survey, Tone Mapping. Digital image processing, Image reconstruction techniques, Psychophysics
Ou, Y.F.[Ya-Fei], Ambalathankandy, P.[Prasoon], Takamaeda, S.[Shinya], Motomura, M.[Masato], Asai, T.[Tetsuya], Ikebe, M.[Masayuki],
Real-Time Tone Mapping: A Survey and Cross-Implementation Hardware Benchmark,
CirSysVideo(32), No. 5, May 2022, pp. 2666-2686.
IEEE DOI
Survey, Tone Mapping. Graphics processing units, Field programmable gate arrays, Hardware, Dynamic range, Imaging, Image sensors, Real-time systems, GPU
Woods, J.W.[John W.], (Ed.)
Subband Image Coding,
KluwerAcademic Publishers, Norwell, MA, December 1990. Referenced as: SubCodingISBN 0-7923-9093-8
WWW Link. Survey, Subband.
JPEG 2000,
Code, Image Processing.
HTML Version. Survey, JPEG. The standards organization page for JPEG 2000.
Lee, D.T.[Daniel T.],
JPEG 2000: Retrospective and New Developments,
PIEEE(93), No. 1, January 2005, pp. 32-41.
IEEE DOI
Survey, JPEG 2000.
Yuen, M., Wu, H.R.,
A Survey of Hybrid MC/DPCM/DCT Video Coding Distortions,
SP(70), No. 3, November 1998, pp. 247-278.
Survey, Compression. Survey, Coding.
Chee, Y.K.[Y. Kheong],
Survey of progressive image transmission methods,
IJIST(10), No. 1, 1999, pp. 3-19. Survey, Progressive Transmission.
Moffat, A.[Alistair],
Huffman Coding,
Surveys(52), No. 4, September 2019, pp. Article No 85.
DOI Link
Survey, Huffman Coding.
Pinho, A.J.[Armando J.], Neves, A.J.R.[António J.R.],
A Survey on Palette Reordering Methods for Improving the Compression of Color-Indexed Images,
IP(13), No. 11, November 2004, pp. 1411-1418.
IEEE DOI Survey, Compression.
Earlier:
Palette reordering under an exponential power distribution model of prediction residuals,
ICIP04(I: 501-504).
IEEE DOI
Earlier:
JPEG 2000 coding of color-quantized images,
ICIP03(II: 181-184).
IEEE DOI
Wei, X.[Xuekai], Zhou, M.L.[Ming-Liang], Wang, H.Q.[He-Qiang], Yang, H.Y.[Hao-Yan], Chen, L.[Lei], Kwong, S.[Sam],
Recent Advances in Rate Control: From Optimization to Implementation and Beyond,
CirSysVideo(34), No. 1, January 2024, pp. 17-33.
IEEE DOI
Survey, Rate Control.
Section, Multiple Entries: 5.5.2 Motion Coding, Video Coding, Evaluations, Surveys Chapter Contents (Back)
Evaluation, Motion Coding. Evaluation, Video Coding. Compression, Video. Survey, Compression. Survey, Video Coding.
Pearson, D.E.,
Developments in Model-Based Video Coding,
PIEEE(83), No. 6, June 1995, pp. 892-906. Survey, Video Coding.
Pirsch, P., Demassieux, N., Gehrke, W.,
VLSI Architectures for Video Compression: A Survey,
PIEEE(83), No. 2, February 1995, pp. 220-246. Survey, Video Coding.
Yeh, C.H.[Chia-Hung], Wun, M.T.[Ming-Te], Chou, B.Y.[Bo-Yin],
Motion Estimation in Video Coding: A Systematic Review, Classification and Evaluation,
RPCS(2), No. 3, November 2009, pp. 178-194.
WWW Link.
Survey, Video Coding.
Ji, R.[Ruolei], Karam, L.J.[Lina J.],
Learning-based Visual Compression,
FTCGV(15), No. 1, 2023, pp. 1-112.
DOI Link
Survey, Compression.
Begen, A.C.[Ali C.], Akgul, T.[Tankut], Baugher, M.[Mark],
Watching Video over the Web Part 1: Streaming Protocols,
Internet(15), No. 2, March/April 2011, pp. 54-63.
IEEE DOI
And:
Watching Video over the Web Part 2: Applications, Standardization, and Open Issues,
Internet(15), No. 3, May/June 2011, pp. 59-63.
IEEE DOI Survey, Streaming Video.
Lee, R.[Royson], Venieris, S.I.[Stylianos I.], Lane, N.D.[Nicholas D.],
Deep Neural Network–Based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions,
Surveys(54), No. 8, October 2021, pp. xx-yy.
DOI Link
Survey, Streaming. Deep learning, content delivery networks, distributed systems
Section, Multiple Entries: 5.5.3.8 Transmission Issues, Special Issues, Surveys Chapter Contents (Back)
Transmission. Television. Survey, Transmission.
Zhai, F.[Fan], Katsaggelos, A.K.[Aggelos K.],
Joint Source-Channel Video Transmission,
Morgan Claypool2007. Synthesis Lectures on Image, Video, and Multimedia Processing Survey, Video Transmission.
WWW Link.
Moving Picture Experts Group,
2007.
WWW Link. Survey, MPEG. Vendor, MPEG. The MPEG website.
Srinivasan, U., Pfeiffer, S., Nepal, S., Lee, M., Gu, L., and Barrass, S.,
A survey of MPEG-1 audio, video and semantic analysis techniques,
MultToolApp(27), No. 1, 2005, pp. 105-141. Survey, MPEG.
MPEG Industry Forum,
2007.
WWW Link. Survey, MPEG. Society, Image Analysis. To further the adoption of MPEG Standards, by establishing them as well accepted and widely used standards among creators of content, developers, manufacturers, providers of services, and end users. Industrial users.
van der Schaar, M.[Mihaela], Turaga, D.S.[Deepak S.], Stockhammer, T.[Thomas],
MPEG-4 Beyond Conventional Video Coding: Object Coding, Resilience, and Scalability,
Morgan Claypool2006. Synthesis Lectures on Image, Video, and Multimedia Processing Survey, MPEG-4.
WWW Link.
Sullivan, G.J., Wiegand, T.[Thomas],
Video Compression: From Concepts to the H.264/AVC Standard,
PIEEE(93), No. 1, January 2005, pp. 18-31.
IEEE DOI
Survey, H.264. Survey, AVC.
Ma, S.W.[Si-Wei], Huang, T.J.[Tie-Jun], Reader, C., Gao, W.[Wen],
AVS2: Making Video Coding Smarter,
SPMag(32), No. 2, March 2015, pp. 172-183.
IEEE DOI
Survey, AVS Coding. Standards in a Nutshell article. computer vision
Yu, L.[Lu], Chen, S.[Sijia], Wang, J.P.[Jian-Peng],
Overview of AVS-video coding standards,
SP:IC(24), No. 4, April 2009, pp. 247-262.
Elsevier DOI
Survey, AVS Coding. AVS; Video coding; Standard
Dufaux, F.[Frederic], Gao, W.[Wen], Tubaro, S.[Stefano], Vetro, A.[Anthony],
Distributed Video Coding: Trends and Perspectives,
JIVP(2009), No. 2009, pp. xx-yy.
DOI Link
Survey, Video Coding.
Section, Multiple Entries: 5.5.8.1.7 Motion Compensation, Low Bit Rate, Survey, Evaluations Chapter Contents (Back)
Low Bit Rate Coding. Survey, Motion Coding. Evaluation, Motion Coding.
Mattavelli, M.[Marco], Amer, I.[Ihab], Raulet, M.,
The Reconfigurable Video Coding Standard,
SPMag(27), No. 3, 2010, pp. 159-167.
IEEE DOI
Survey, Video Coding. Standards in a Nutshell article.
Baraniuk, R.G., Goldstein, T., Sankaranarayanan, A.C., Studer, C., Veeraraghavan, A., Wakin, M.B.,
Compressive Video Sensing: Algorithms, architectures, and applications,
SPMag(34), No. 1, January 2017, pp. 52-66.
IEEE DOI
Survey, Compressive Sensing. compressed sensing
Tseng, P., Chang, Y., Huang, Y., Fang, H., Huang, C., Chen, L.,
Advances in Hardware Architectures for Image and Video Coding: A Survey,
PIEEE(93), No. 1, January 2005, pp. 184-197.
IEEE DOI
Survey, Compression.
Xin, J., Lin, C.W., Sun, M.T.,
Digital Video Transcoding,
PIEEE(93), No. 1, January 2005, pp. 84-97.
IEEE DOI
Survey, Transcoding.
Lecca, M.[Michela], Torresani, A.[Alessandro], Remondino, F.[Fabio],
Comprehensive evaluation of image enhancement for unsupervised image description and matching,
IET-IPR(14), No. 16, 19 December 2020, pp. 4329-4339.
DOI Link
Survey, Image Enhancement.
Earlier:
On Image Enhancement for Unsupervised Image Description and Matching,
CIAP19(II:82-92).
Springer DOI
Section, Multiple Entries: 5.6.1 Image Enhancement, Survey, Review, Evaluation Chapter Contents (Back)
Image Enhancement. Enhancement. Evaluation. Survey, Image Enhancement.
Wang, D.C.C.[David C.C.], Vagnucci, A.H.[Anthony H.], and Li, C.C.,
Digital Image Enhancement: A Survey,
CVGIP(24), No. 3, December 1983, pp. 363-381.
Elsevier DOI Image Enhancement, Survey. Survey, Image Enhancement.
Amato, I.[Ivan],
Lying with Pixels,
Technology Review(103), No. 4, July 2000, pp. 60-66. Survey, Image Analysis. Survey of systems that add, change, delete information from live video (especially sports events).
Jarvis, J.F., Judice, C.N., Ninke, W.H.,
A Survey of Techniques for the Display of Continuous Tone Pictures on Bilevel Displays,
CGIP(5), No. 1, March 1976, pp. 13-40. Survey, Display.
Elsevier DOI
Niall, K.K.[Keith K.], (Ed.)
Vision and Displays for Military and Security Applications: The Advanced Deployable Day/Night Simulation Project,
Springer2010, ISBN: 978-1-4419-1722-5
WWW Link. Survey, Displays. Buy this book: Vision and Displays for Military and Security Applications: The Advanced Deployable Day/Night Simulation Project
Chen, J.L.[Jang-Lin], Cranton, W.[Wayne], Fihn, M.[Mark], (Eds.)
Handbook of Visual Display Technology,
SpringerNew-York, 2012.
 
|
WWW Link.
Survey, Displays. Detailed reference on display technology and devices.
Lau, D.L.[Daniel L.], Arce, G.R.[Gonzalo R.],
Modern Digital Halftoning,
CRC PressApril, 2008, ISBN: 9781420047530 Survey, Halftone.
WWW Link. Second Edition. Buy this book: Modern Digital Halftoning, Second Edition (Signal Processing and Communications)
Sutton, M.A.[Michael A.], Orteu, J.J.[Jean-José], Schreier, H.[Hubert],
Image Correlation for Shape, Motion and Deformation Measurements Basic Concepts,Theory and Applications,
Springer2009. ISBN: 978-0-387-78746-6
WWW Link. Survey, Correlation.
Buy this book: Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts,Theory and Applications
Brunelli, R.[Roberto],
Template Matching Techniques in Computer Vision: Theory and Practice,
WileyMay 2009. ISBN: 978-0-470-51706-2.
HTML Version. Survey, Template Matching. Buy this book: Template Matching Techniques in Computer Vision: Theory and Practice
Section, Multiple Entries: 12.1.3.1 Image Registration -- Overview, Survey, Review, Evaluation, Comparison Chapter Contents (Back)
Survey, Registration. Image Registration. Evaluation, Registration.
Brown, L.G.[Lisa G.],
A Survey of Image Registration Techniques,
Surveys(24), No. 4, December 1992, pp. 325-376. Survey, Registration. Large set of references on registration. Correlation, Fourier, point mapping, elastic models.
Lester, H.[Hava], Arridge, S.R.[Simon R.],
A survey of hierarchical non-linear medical image registration,
PR(32), No. 1, January 1999, pp. 129-149.
Elsevier DOI Survey, Registration. 3 types of hierarchical non-linear registration.
Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.,
Mutual-information-based registration of medical images: a survey,
MedImg(22), No. 8, August 2003, pp. 986-1004.
IEEE Abstract.
Survey, Registration.
Maintz, J.B.A., Viergever, M.A.,
A Survey of Medical Image Registration,
MIA(2), No. 1, 1998, pp. 1-16. Survey, Registration.
Zitova, B.[Barbara], Flusser, J.[Jan],
Image Registration Methods: A Survey,
IVC(21), No. 11, October 2003, pp. 977-1000.
Elsevier DOI
Survey, Registration. Setps: feature detection, feature matching, mapping function, image transformation.
CVonline: Recognition and Registration Methods,
CV-OnlineJuly 2001.
HTML Version. Survey, Registration. Survey, Recognition.
Ericsson, A.[Anders], Karlsson, J.[Johan],
Measures for Benchmarking of Automatic Correspondence Algorithms,
JMIV(28), No. 3, July 2007, pp. 225-241.
Springer DOI
Survey, Matching.
Earlier: A2, A1:
A Ground Truth Correspondence Measure for Benchmarking,
ICPR06(III: 568-573).
IEEE DOI
And: A1, A2:
Benchmarking of algorithms for automatic correspondence localisation,
BMVC06(II:759).
PDF File.
Shams, R., Sadeghi, P., Kennedy, R.A., Hartley, R.I.,
A Survey of Medical Image Registration on Multicore and the GPU,
SPMag(27), No. 2, 2010, pp. 50-60.
IEEE DOI
Survey, Registration.
Goshtasby, A.A.[A. Ardeshir],
Image Registration: Principles, Tools and Methods,
Springer2012. ISBN 978-1-4471-2457-3
WWW Link. Survey, Registration.
Buy this book: Image Registration: Principles, Tools and Methods (Advances in Computer Vision and Pattern Recognition)
See also 2-D and 3-D Image Registration: For Medical, Remote Sensing, and Industrial Applications.
Dawn, S.[Suma], Saxena, V.[Vikas], Sharma, B.[Bhudev],
Remote Sensing Image Registration Techniques: A Survey,
ICISP10(103-112).
Springer DOI
Survey, Registration.
Sotiras, A., Davatzikos, C., Paragios, N.,
Deformable Medical Image Registration: A Survey,
MedImg(32), No. 7, 2013, pp. 1153-1190.
IEEE DOI
Survey, Image Registration. image fusion
Mitchell, H.B.,
Image Fusion: Theories, Techniques and Applications,
Springer2010, ISBN: 978-3-642-11215-7.
WWW Link. Buy this book: Image Fusion: Theories, Techniques and Applications
Survey, Image Fusion.
Liu, J.J.[Jia-Jun], Huang, Z.[Zi], Cai, H.Y.[Hong-Yun], Shen, H.T.[Heng Tao], Ngo, C.W.[Chong Wah], Wang, W.[Wei],
Near-duplicate video retrieval: Current research and future trends,
Surveys(45), No. 2, February 2013, pp. Article No 44.
DOI Link
Survey, Video Duplicates. The exponential growth of online videos, along with increasing user involvement in video-related activities, has been observed as a constant phenomenon during the last decade. User's time spent on video capturing, editing, uploading, searching, and viewing
Wary, A.[Alongbar], Neelima, A.[Arambam],
A review on robust video copy detection,
MultInfoRetr(8), No. 2, June 2019, pp. 61-78.
Springer DOI
Survey, Video Copy.
Section, Multiple Entries: 12.1.4.1 Image and Sensor Fusion -- Review and Survey Articles, Evaluations Chapter Contents (Back)
Survey, Sensor Fusion. Sensor Fusion. Fusion.
Bloch, I.,
Information Combination Operators for Data Fusion: A Comparative Review with Classification,
SMC-A(26), No. 1, January 1996, pp. 52-67.
IEEE Top Reference. Survey, Data Fusion.
CVonline: Sensor Fusion, Registration and Planning,
CV-OnlineJuly 2001.
HTML Version. Survey, Fusion. Survey, Registration.
Pajares, G.[Gonzalo], Manuel de la Cruz, J.[Jesus],
A wavelet-based image fusion tutorial,
PR(37), No. 9, September 2004, pp. 1855-1872.
Elsevier DOI
Survey, Image Fusion. multiscale-decomposition (
See also Categorization of Multiscale-Decomposition-Based Image Fusion Schemes with a Performance Study for a Digital Camera Application, A. ) Wavelet:
See also Multisensor Image Fusion Using the Wavelet Transform. ARSIS.
See also Image fusion: The ARSIS concept and some successful implementation schemes.
Liu, Z.[Zheng], Blasch, E.[Erik], Xue, Z.Y.[Zhi-Yun], Zhao, J.Y.[Ji-Ying], Laganiere, R.[Robert], Wu, W.[Wei],
Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study,
PAMI(34), No. 1, January 2012, pp. 94-109.
IEEE DOI
Survey, Inage Fusion. Study 12 fusion metrics and images with distortion. Analysis can be applied to other sets of metrics, etc.
Bleiholder, J.[Jens], Naumann, F.[Felix],
Data fusion,
Surveys(41), No. 1, December 2008, pp. 1-41.
WWW Link.
Survey, Sensor Fusion.
Garzelli, A.[Andrea],
A Review of Image Fusion Algorithms Based on the Super-Resolution Paradigm,
RS(8), No. 10, 2016, pp. 797.
DOI Link
Survey, Fusion.
Peng, Y.[Ying], Qin, Y.[Yechen], Tang, X.L.[Xiao-Lin], Zhang, Z.Q.[Zhi-Qiang], Deng, L.[Lei],
Survey on Image and Point-Cloud Fusion-Based Object Detection in Autonomous Vehicles,
ITS(23), No. 12, December 2022, pp. 22772-22789.
IEEE DOI
Survey, Point Cloud Fusion. Object detection, Feature extraction, Cameras, Autonomous vehicles, Detectors, Laser radar, Deep learning, Autonomous vehicle, point-cloud
Gomez-Chova, L., Tuia, D., Moser, G., Camps-Valls, G.,
Multimodal Classification of Remote Sensing Images: A Review and Future Directions,
PIEEE(103), No. 9, September 2015, pp. 1560-1584.
IEEE DOI
Survey, Sensor Fusion. Image fusion
Lahat, D., Adali, T., Jutten, C.,
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects,
PIEEE(103), No. 9, September 2015, pp. 1449-1477.
IEEE DOI
Survey, Data Fusion. Data integration
Alparone, L., Wald, L., Chanussot, J., Thomas, C., Gamba, P., Bruce, L.M.,
Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest,
GeoRS(45), No. 10, October 2007, pp. 3012-3021.
IEEE DOI
Survey, Pan-Sharpening.
Nikolakopoulos, K.G.[Konstantinos G.],
Comparison of Nine Fusion Techniques for Very High Resolution Data,
PhEngRS(74), No. 5, May 2008, pp. 647-660.
WWW Link.
Survey, Pansharpening. The efficiency of nine fusion techniques: IHS, Modified IHS, PCA, Pansharp, Wavelet, Local Mean Matching, Local Mean and Variance Matching, Brovey, and Miltiplicative for fusion of QuickBird data.
Thomas, C., Ranchin, T., Wald, L., Chanussot, J.,
Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics,
GeoRS(46), No. 5, May 2008, pp. 1301-1312.
IEEE DOI
Survey, Sensor Fusion.
Dadrass Javan, F.[Farzaneh], Samadzadegan, F.[Farhad], Mehravar, S.[Soroosh], Toosi, A.[Ahmad], Khatami, R.[Reza], Stein, A.[Alfred],
A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery,
PandRS(171), 2021, pp. 101-117.
Elsevier DOI
Survey, Pansharpening. Image fusion, Pan-sharpening, Spectral and spatial quality
Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.,
Image Change Detection Algorithms: A Systematic Survey,
IP(14), No. 3, March 2005, pp. 294-307.
IEEE DOI
Survey, Change Detection.
Shafique, A.[Ayesha], Cao, G.[Guo], Khan, Z.[Zia], Asad, M.[Muhammad], Aslam, M.[Muhammad],
Deep Learning-Based Change Detection in Remote Sensing Images: A Review,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
Survey, Change Detection.
Brady, J.P., Nandhakumar, N., and Aggarwal, J.K.,
Recent Progress in the Recognition of Objects from Range Data,
IVC(7), No. 4, November 1989, pp. 295-307.
Elsevier DOI
Earlier: ICPR88(I: 85-92).
IEEE DOI
And: Univ. of Texas-TR-88-1-46, January 1988. Survey, Descriptions, Three-Dimensional. Descriptions, Three-Dimensional. Recognize Range Data.
Prokop, R.J.[Richard J.], Reeves, A.P.[Anthony P.],
A Survey of Moment-Based Techniques for Unoccluded Object Representation and Recognition,
GMIP(54), No. 5, September 1992, pp. 438-460. Survey, Moments.
Kaur, P.[Parminder], Pannu, H.S.[Husanbir Singh], Malhi, A.K.[Avleen Kaur],
Comprehensive Study of Continuous Orthogonal Moments: A Systematic Review,
Surveys(52), No. 4, September 2019, pp. Article No 67.
DOI Link
Survey, Moments.
Flusser, J.[Jan], Zitova, B.[Barbara], Suk, T.[Tomas],
Moments and Moment Invariants in Pattern Recognition,
WileyDecember 2009. ISBN: 978-0-470-69987-4
HTML Version.
Survey, Moments. Buy this book: Moments and Moment Invariants in Pattern Recognition
Numerical computation methods.
Qi, S.[Shuren], Zhang, Y.S.[Yu-Shu], Wang, C.[Chao], Zhou, J.T.[Jian-Tao], Cao, X.C.[Xiao-Chun],
A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation,
Surveys(55), No. 1, January 2023, pp. xx-yy.
DOI Link
Survey, Moments. image representation, fast computation, orthogonal moments, Pattern recognition, geometric invariance
Munsell, B.C.[Brent C.], Dalal, P.[Pahal], Wang, S.[Song],
Evaluating Shape Correspondence for Statistical Shape Analysis: A Benchmark Study,
PAMI(30), No. 11, November 2008, pp. 2023-2039.
IEEE DOI
Survey, Shape Matching. Create a set of synthetic shapes and apply matching. Gives 3 measures from Davies (
See also Learning Shape: Optimal Models for Analysing Natural Variability. ). Evaluate 5 techniques: Richardson and Wang (SDI) (
See also Nonrigid Shape Correspondence Using Landmark Sliding, Insertion and Deletion. ). Thodberg (T-MDL) (
See also Minimum Description Length Shape and Appearance Models. ) Karlsson and Ericsson (E-MDL) and (E-MDL+ curvature) and Euclidean Distance. (
See also Measures for Benchmarking of Automatic Correspondence Algorithms. )
Salvi, J.[Joaquim], Matabosch, C.[Carles], Fofi, D.[David], Forest, J.[Josep],
A review of recent range image registration methods with accuracy evaluation,
IVC(25), No. 5, 1 May 2007, pp. 578-596.
Elsevier DOI Survey, Range Registration.
Earlier: A2, A3, A1, A4:
Registration of Moving Surfaces by Means of One-Shot Laser Projection,
IbPRIA05(I:145).
Springer DOI
3D reconstruction; Range image; Registration
Díez, Y.[Yago], Roure, F.[Ferran], Lladó, X.[Xavier], Salvi, J.[Joaquim],
A Qualitative Review on 3D Coarse Registration Methods,
Surveys(47), No. 3, April 2015, pp. Article No 45.
DOI Link
Survey, Range Registration. 3D registration or matching is a crucial step in 3D model reconstruction. Registration applications span along a variety of research fields, including computational geometry, and geometric modeling.
Damas, S.[Sergio], Cordón, O.[Oscar], Ibáñez, O.[Oscar], Santamaría, J.[Jose], Alemán, I.[Inmaculada], Botella, M.[Miguel], Navarro, F.[Fernando],
Forensic identification by computer-aided craniofacial superimposition: A survey,
Surveys(43), No. 4, October 2011, pp. xx-yy.
DOI Link
Survey, Alignment. Craniofacial superimposition is a forensic process in which a photograph of a missing person is compared with a skull found to determine its identity. After one century of development, craniofacial superimposition has become an interdisciplinary research
Flynn, P.J., Jain, A.K.,
Three-Dimensional Object Recognition,
HPRIP-CV94(497-541). General review, survey. Survey, 3D.
Bowyer, K.W., and Dyer, C.R.,
Aspect Graphs: an Introduction and Survey of Recent Results,
IJIST(2), 1990, pp. 315-328. Survey, Aspect Graph. Survey of aspect graph matching.
Rieger, J.H., Rohr, K.,
Semi-Algebraic Solids in 3-Space: A Survey of Modeling Schemes and Implications for View Graphs,
IVC(12), No. 7, September 1994, pp. 395-410.
Elsevier DOI Survey, 3D.
Reiss, T.H.[Thomas H.],
Recognizing Planar Objects Using Invariant Image Features,
Springer-Verlag1993. LNCS676. Survey, Invariants.
Springer DOI Based on thesis.
Section, Multiple Entries: 13.1.2.7 Invariance Papers -- Mundy Chapter Contents (Back)
Object Recognition. Invariants. Matching, Invariants. Mundy, J.L., and Zisserman, A.,
Geometric Invariance in Computer Vision,
Cambridge, MA: MIT Press1992, 560pp. Indexed as GICV92 Book Survey, Invariance. Invariance, Survey. References should be under GICV. A collection of papers on geometric invariants and their application to computer vision. Sections on Algebraic, non-algebraic, multiple views, and applications.
Quinn, M.J., Deo, N.,
Parallel Graph Algorithms,
Surveys(16), No. 3, September 1984, pp. 319-348. Survey, Graph Matching.
Bouhenni, S.[Sarra], Yahiaoui, S.[Said], Nouali-Taboudjemat, N.[Nadia], Kheddouci, H.[Hamamache],
A Survey on Distributed Graph Pattern Matching in Massive Graphs,
Surveys(54), No. 2, February 2021, pp. xx-yy.
DOI Link
Survey, Graph Matching. graph simulation, subgraph isomorphism, distributed graphs, Graph pattern matching
Grohe, M.[Martin], Schweitzer, P.[Pascal],
The Graph Isomorphism Problem,
CACM(63), No. 11, November 2020, pp. 128-134.
DOI Link
Survey, Graph Isomorphism.
Chang, X.J.[Xiao-Jun], Ren, P.Z.[Peng-Zhen], Xu, P.F.[Peng-Fei], Li, Z.H.[Zhi-Hui], Chen, X.J.[Xiao-Jiang], Hauptmann, A.[Alex],
A Comprehensive Survey of Scene Graphs: Generation and Application,
PAMI(45), No. 1, January 2023, pp. 1-26.
IEEE DOI
Survey, Graphs. Visualization, Task analysis, Feature extraction, Image recognition, Cognition, Training, Systematics, Scene graph, visual relationship recognition
Kittler, J.V., and Illingworth, J.,
Relaxation Labelling Algorithms: A Review,
IVC(3), No. 4, November 1985, pp. 206-216.
Elsevier DOI Survey, Relaxation. Relaxation, Survey.
Davis, L.S., and Rosenfeld, A.,
Cooperating Processes for Low-Level Vision: A Survey,
AI(17), No. 1-3, August 1981, pp. 245-263.
Elsevier DOI Survey, Relaxation. Relaxation, Survey.
Rosenfeld, A.,
Iterative Methods in Image Analysis,
PR(10), No. 3, 1978, pp. 181-187.
Elsevier DOI Survey, Relaxation. Relaxation, Survey.
Earlier: PRIP77(14-18).
And:
Relaxation Methods in Image Processing and Analysis,
ICPR78(181-185).
Kumar, V.,
Algorithms for Constraint-Satisfaction Problems: A Survey,
AIMag(13), No. 1, Spring 1992, pp. 32-44. Survey, Relaxation. Survey, Constraint Satisfaction. Relaxation, Survey. Constraint Satisfaction, Survey.
Shapiro, L.G.,
Relational Matching,
HPRIP-CV94(475-496).
Earlier: With: Haralick, R.M., AppOpt(26), No. 10, May 15, 1987, pp. 1845-1851. Survey, Matching. Matching, Survey. A survey (overview) of relational matching with only their references.
Chávez, E.[Edgar], Navarro, G.[Gonzalo], Baeza-Yates, R.[Ricardo], Marroquín, J.L.[José Luis],
Searching in metric spaces,
Surveys(33), No. 3, September 2001, pp. 273-321.
WWW Link.
Survey, Distance Measures.
Jain, R.C., and Haynes, S.M.,
Imprecision in Computer Vision,
Computer(15), No. 8, August 1982, pp. 39-48. Uncertainty. Survey, Uncertainty. General survey about how it is used.
Pawlak, Z.[Zdzislaw], Grzymala-Busse, J.[Jerzy], Slowinski, R.[Roman], Ziarko, W.[Wojciech],
Rough sets,
CACM(38), No. 11, November 1995, pp. 88-95.
DOI Link Survey, Rough Sets.
Pal, S.K.[Sankar K.], Peters, J.F.[James F.], (Eds.)
Rough Fuzzy Image Analysis: Foundations and Methodologies,
CRC PressBoca Raton, FL, May 04, 2010 ISBN: 9781439803295.
WWW Link. Buy this book: Rough Fuzzy Image Analysis: Foundations and Methodologies (Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series)
Survey, Fuzzy Sets.
Bielza, C.[Concha], Larrañaga, P.[Pedro],
Discrete Bayesian Network Classifiers: A Survey,
Surveys(47), No. 1, July 2014, pp. Article No 5.
DOI Link
Survey, Bayes Nets. Survey the whole set of discrete Bayesian network classifiers devised to date, organized in increasing order of structure complexity: naive Bayes, selective naive Bayes, seminaive Bayes, one-dependence Bayesian classifiers, k-dependence Bayesian classifiers, Bayesian network-augmented naive Bayes, Markov blanket-based Bayesian classifier, unrestricted Bayesian classifiers, and Bayesian multinets. Issues of feature subset selection and generative and discriminative structure and parameter learning are also covered.
Neuhaus, M.[Michel], Bunke, H.[Horst],
Bridging the Gap Between Graph Edit Distance and Kernel Machines,
World ScientificSeptember 2007. ISBN: 978-981-270-817-5 Survey, Graph Matching. Buy this book: Bridging the Gap Between Graph Edit Distance and Kernel Machines (Series in Machine Perception and Artificial Intelligence)
Bille, P.,
A survey on tree edit distance and related problems,
TCS(337), No. 1-3, 2005, pp. 217-239.
Elsevier DOI Survey, Tree Edit.
Jolliffe, I.T.,
Principal Component Analysis,
SpringerNew-York, 2002. ISBN: 978-0-387-95442-4.
WWW Link. Buy this book: Principal Component Analysis
Earlier: First edition: Springer-VerlagNew-York, 1986. Survey, PCA. The Book, overview.
Gao, X., Su, Y., Li, X., Tao, D.,
A Review of Active Appearance Models,
SMC-C(40), No. 2, March 2010, pp. 145-158.
IEEE DOI
Survey, AAM. Survey, Active Appearance Models.
Section, Multiple Entries: 13.4.1.7 Surveys, Comparisons, Evaluations, Principal Components Chapter Contents (Back)
PCA. Principal Components. Evaluation, Principal Components. Survey, PCA. Survey, Pricnipal Components.
Elad, M.[Michael],
Sparse and Redundant Representations From Theory to Applications in Signal and Image Processing,
Springer2010, ISBN: 978-1-4419-7010-7
WWW Link. Survey, Invariants. Buy this book: Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Oreifej, O.[Omar], Shah, M.[Mubarak],
Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications,
 
|
WWW Link.
Survey, Low-Rank Optimization.
Bergqvist, G., Larsson, E.G.,
The Higher-Order Singular Value Decomposition: Theory and an Application,
SPMag(27), No. 3, 2010, pp. 151-154.
IEEE DOI
Survey, SVD. Lecture Notes
Madarkar, J.[Jitendra], Sharma, P.[Poonam], Singh, R.P.[Rimjhim Padam],
Sparse representation for face recognition: A review paper,
IET-IPR(15), No. 9, 2021, pp. 1825-1844.
DOI Link
Survey, Face Recognition.
Chin, R.T., and Dyer, C.R.,
Model-Based Recognition in Robot Vision,
Surveys(18), No. 1, March 1986, pp. 67-108. Knowledge-Based Vision. Model Based Vision. Survey, Matching. Survey, Robot Vision. Matching, Models. Robot Vision, Survey.
Earlier:
Model-Based Industrial Part Recognition: Systems and Algorithms,
Univ. of WisconsinTR 538, March 1984. Comparison of various techniques organized by 2-D, 2.5-D and 3-D representations considering feature extraction, modeling and matching for each.
Arman, F., and Aggarwal, J.K.,
Model-Based Object Recognition in Dense Range Images,
Surveys(25), No. 1, March 1993, pp. 5-43. Survey, Recognition.
See also Segmentation of 3-D Range Images Using Pyramidal Data Structures.
Andreopoulos, A.[Alexander], Tsotsos, J.K.[John K.],
50 Years of object recognition: Directions forward,
CVIU(117), No. 8, August 2013, pp. 827-891.
Elsevier DOI
Survey, Object Recognition. Active vision; Object recognition; Object representations; Object learning; Dynamic vision; Cognitive vision systems 50 year revie, limitations, drawbacks, future directions.
Perrott, C.G.[Chris G.], Hamey, L.G.C.[Leonard G.C.],
Object Recognition: A Survey of the Literature,
MacQuarie Univ.1991. Survey, Recognition.
Pope, A.R.[Arthur R.],
Model-Based Object Recognition: A Survey of Recent Research,
UBCTR-94-04, January 1994. Survey, Recognition.
Cohn, A.G.,
The Challenge of Qualitative Spatial Reasoning,
Surveys(27), No. 3, September 1995, pp. 323-325. Survey, Spatial Reasoning.
Section, Multiple Entries: 13.6.1.1 Knowledge-Based Vision, Surveys, Overviews Chapter Contents (Back)
Survey, Knowledge-Based Vision. Knowledge. Knowledge-Based Vision.
Binford, T.O.,
Survey of Model-Based Image Analysis Systems,
IJRR(1), No. 1, Spring 1982, pp. 18-64. Survey, Model Based Vision. Model Based Vision. Survey, Model Based Vision.
Crevier, D., Lepage, R.,
Knowledge-Based Image Understanding Systems: A Survey,
CVIU(67), No. 2, August 1997, pp. 161-185.
DOI Link
Survey, Knowledge-Based Vision.
CVonline: Scene Understanding,
CV-OnlineJuly 2001.
HTML Version. Survey, Image Understanding.
Xie, L.[Lin], Lee, F.F.[Fei-Fei], Liu, L.[Li], Kotani, K.[Koji], Chen, Q.[Qiu],
Scene recognition: A comprehensive survey,
PR(102), 2020, pp. 107205.
Elsevier DOI
Survey, Scene Recognition. Scene recognition, Patch feature encoding, Spatial layout pattern learning, Deep learning
Patricio, C.[Cristiano], Neves, J.C.[Joao C.], Teixeira, L.F.[Luis F.],
Explainable Deep Learning Methods in Medical Image Classification: A Survey,
Surveys(56), No. 4, October 2023, pp. xx-yy.
DOI Link
Survey, Explainable AI. Explainable AI, interpretability, explainability, deep learning, medical image analysis
Gou, J.P.[Jian-Ping], Yu, B.S.[Bao-Sheng], Maybank, S.J.[Stephen J.], Tao, D.C.[Da-Cheng],
Knowledge Distillation: A Survey,
IJCV(129), No. 6, June 2021, pp. 1789-1819.
Springer DOI
Survey, Knowledge Distillation.
Li, Z.H.[Zhi-Hui], Xu, P.F.[Peng-Fei], Chang, X.J.[Xiao-Jun], Yang, L.[Luyao], Zhang, Y.Y.[Yuan-Yuan], Yao, L.[Lina], Chen, X.J.[Xiao-Jiang],
When Object Detection Meets Knowledge Distillation: A Survey,
PAMI(45), No. 8, August 2023, pp. 10555-10579.
IEEE DOI
Survey, Knowledge Distillation. Task analysis, Computational modeling, Analytical models, Image coding, Solid modeling, Object detection, weakly supervised object detection
Hassanin, M.[Mohammed], Khan, S.[Salman], Tahtali, M.[Murat],
Visual Affordance and Function Understanding: A Survey,
Surveys(54), No. 3, April 2021, pp. xx-yy.
DOI Link
Survey, Visual Function. Discover information, understand it, and interact with the environment. deep learning, Affordance prediction, functional scene understanding, visual reasoning
Aloimonos, Y., and Shulman, D.,
Integration of Visual Modules: An Extension of the Marr Paradigm,
San Diego: Academic Press1989. Survey, Computational Vision. Computational Vision, Survey. A Chapter as a paper (first author only):
Unification and Integration of Visual Modules: An Extension of the Marr Paradigm,
DARPA89(507-551). A Chapter in the above book. The goal is to provide a framework to discuss computational algorithms. Included are discontinuous regularization, etc.
Srihari, R.K.,
Computational Models for Integrating Linguistic and Visual Information: A Survey,
AIR(8), No. 5-6, 1995, pp. 349-369. Survey, Computational Vision.
Suetens, P., Fua, P.V., and Hanson, A.J.,
Some Computational Strategies for Object Recognition,
Surveys(24), No. 1, March 1992, pp. 5-62. Survey, Matching. Matching, Survey. Covers a number of different recognition techniques both from SRI and many other locations. The survey is dated to about 1989.
Hossain, M.Z.[Md. Zakir], Sohel, F.[Ferdous], Shiratuddin, M.F.[Mohd Fairuz], Laga, H.[Hamid],
A Comprehensive Survey of Deep Learning for Image Captioning,
Surveys(51), No. 6, February 2019, pp. Article No 118.
DOI Link
Survey, Captioning.
Stefanini, M.[Matteo], Cornia, M.[Marcella], Baraldi, L.[Lorenzo], Cascianelli, S.[Silvia], Fiameni, G.[Giuseppe], Cucchiara, R.[Rita],
From Show to Tell: A Survey on Deep Learning-Based Image Captioning,
PAMI(45), No. 1, January 2023, pp. 539-559.
IEEE DOI
Survey, Image Captions. Visualization, Feature extraction, Task analysis, Convolutional neural networks, Additives, Image coding, Training
Hall, P.A., and Dowling, G.R.,
Approximate String Matching,
Surveys(12), No. 4, December 1980, pp. 381-402. Survey, String Matching.
Navarro, G.[Gonzalo],
A guided tour to approximate string matching,
Surveys(33), No. 1, March 2001, pp. 31-88.
WWW Link.
Survey, String Matching.
Faro, S.[Simone], Lecroq, T.[Thierry],
The exact online string matching problem: A review of the most recent results,
Surveys(45), No. 2, February 2013, pp. Article No 13.
DOI Link
Survey, String Matching.
Section, Multiple Entries: 21.2 Medical Applications -- Surveys Chapter Contents (Back)
Survey, Medical Applications. Medical, Applications.
Zaidi, H., Tsui, B.M.W.,
Review of Computational Anthropomorphic Anatomical and Physiological Models,
PIEEE(97), No. 12, December 2009, pp. 1938-1953.
IEEE DOI
Survey, Anatomical Models.
Wernick, M.N., Bouman, C.A., Leahy, R.M., Duncan, J.S.,
The Roles of Signal Processing in Medical Imaging,
SPMag(27), No. 4, 2010, pp. 12-140.
IEEE DOI
Survey, Medical Imaging. Special issue, the whole thing. Individual papers listed separately.
Toennies, K.D.[Klaus D.],
Guide to Medical Image Analysis: Methods and Algorithms,
Springer2012. ISBN 978-1-4471-2750-5.
WWW Link.
Buy this book: Guide to Medical Image Analysis: Methods and Algorithms (Advances in Computer Vision and Pattern Recognition)
Survey, Medical Images.
Morooka, K.[Ken'ichi], Nakamoto, M.[Masahiko], Sato, Y.[Yoshinobu],
A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures,
IEICE(E96-D), No. 4, April 2013, pp. 784-797.
WWW Link.
Survey, Medical Analysis.
Section, Multiple Entries: 21.1.6 Medical Image Analysis, Books, Special Issues, Introductions, Special Sections, Reviews, Surveys Chapter Contents (Back)
Survey, Medical Image Analysis.
Gulo, C.A.S.J.[Carlos A. S. J.], Sementille, A.C.[Antonio C.], author, o.M.R.S.T.[oão Manuel R. S. TavaresEmail],
Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review,
RealTimeIP(16), No. 6, December 2019, pp. 1891-1908.
Springer DOI
Survey, Medical Image Processing.
Stytz, M.R., Frieder, G., Frieder, O.,
Three-Dimensional Medical Imaging: Algorithms and Computer Systems,
Surveys(23), No. 4, December 1991, pp. 421-499. Survey, Medical Applications.
Meijering, E.,
Cell Segmentation: 50 Years Down the Road,
SPMag(29), No. 5, 2012, pp. 140-145.
IEEE DOI
Survey, Cell Segmentation. Life Sciences. Review of the results.
Mitra, S.[Shyamali], Das, N.[Nibaran], Dey, S.[Soumyajyoti], Chakraborty, S.[Sukanta], Nasipuri, M.[Mita], Naskar, M.K.[Mrinal Kanti],
Cytology Image Analysis Techniques Toward Automation: Systematically Revisited,
Surveys(54), No. 3, April 2021, pp. xx-yy.
DOI Link
Survey, Cytology. Image classification, image segmentation, computer aided diagnosis, malignant and benign
Gurcan, M.N.[Metin N.], Boucheron, L.[Laura], Can, A.[Ali], Madabhushi, A.[Anant], Rajpoot, N.[Nasir], Yener, B.[Bulent],
Histopathological Image Analysis: A Review,
RevBiomedEng(2), 2009, pp. 147-171.
IEEE DOI
WWW Link. Survey, Histopathology.
Section, Multiple Entries: 21.4.8 Surveys, Comparisons, Cells, DNA Chapter Contents (Back)
Survey, DNA. Cells.
Wade, N.J.[Nicholas J.],
Image, eye, and retina,
JOSA-A(24), No. 5, May 2007, pp. 1229-1249.
WWW Link.
Survey, Retinal Imaging. Invited Review.
Allam, A.[Ali], Youssif, A.[Aliaa], Ghalwash, A.[Atef],
Automatic Segmentation of Optic Disc in Eye Fundus Images: A Survey,
ELCVIA(14), No. 1, 2015, pp. xx-yy.
DOI Link
Survey, Optic Disc.
Hall, E.L., Kruger, R.P., Dwyer, III, S.J., Hall, D.L., McLaren, R.W., Lodwick, G.S.,
A Survey of Preprocessing and Feature Extraction Techniques for Radiographic Images,
TC(20), No. 9, September 1971, pp. 1032-1045. Survey, Radiographic Images.
Trussell, H.J.,
Processing of X-Ray Images,
PIEEE(69), No. 5, May 1981, 615-627. Survey, Radiographic Images.
Cheng, H.D., Cai, X.P.[Xiao-Peng], Chen, X.W.[Xiao-Wei], Hu, L.M.[Li-Ming], Lou, X.L.[Xue-Ling],
Computer-aided detection and classification of microcalcifications in mammograms: a survey,
PR(36), No. 12, December 2003, pp. 2967-2991.
Elsevier DOI
Survey, Mammograms.
Xian, M.[Min], Zhang, Y.T.[Ying-Tao], Cheng, H.D., Xu, F.[Fei], Zhang, B.[Boyu], Ding, J.[Jianrui],
Automatic breast ultrasound image segmentation: A survey,
PR(79), 2018, pp. 340-355.
Elsevier DOI
Survey, Ultrasound. Breast ultrasound (BUS) images, Breast cancer, Segmentation, Benchmark, Early detection, Computer-aided diagnosis (CAD)
van Ginneken, B., ter Haar Romeny, B.M., Viergever, M.A.,
Computer-aided diagnosis in chest radiography: a survey,
MedImg(20), No. 12, December 2001, pp. 1228-1241.
IEEE Top Reference.
Survey, Radiography.
Sluimer, I., Schilham, A.M.R., Prokop, M., van Ginneken, B.,
Computer Analysis of Computed Tomography Scans of the Lung: A Survey,
MedImg(25), No. 4, April 2006, pp. 385-405.
IEEE DOI
Survey, Tomography.
Murphy, K., van Ginneken, B., Reinhardt, J.M., Kabus, S., Ding, K., Deng, X., Cao, K., Du, K., Christensen, G.E., Garcia, V., Vercauteren, T., Ayache, N.J., Commowick, O., Malandain, G., Glocker, B., Paragios, N., Navab, N., Gorbunova, V., Sporring, J., de Bruijne, M., Han, X., Heinrich, M.P., Schnabel, J.A., Jenkinson, M., Lorenz, C., Modat, M., McClelland, J.R., Ourselin, S., Muenzing, S.E.A., Viergever, M.A., de Nigris, D., Collins, D.L., Arbel, T., Peroni, M., Li, R., Sharp, G.C., Schmidt-Richberg, A., Ehrhardt, J., Werner, R., Smeets, D., Loeckx, D., Song, G., Tustison, N., Avants, B., Gee, J.C., Staring, M., Klein, S., Stoel, B.C., Urschler, M., Werlberger, M., Vandemeulebroucke, J., Rit, S., Sarrut, D., Pluim, J.P.W.,
Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge,
MedImg(30), No. 11, November 2011, pp. 1901-1920.
IEEE DOI
Survey, Registration.
Kumar, D.[Dileep], Gandhamal, A.[Akash], Talbar, S.[Sanjay], Hani, A.F.M.[Ahmad Fadzil Mohd],
Knee Articular Cartilage Segmentation from MR Images: A Review,
Surveys(51), No. 5, January 2019, pp. Article No 97.
DOI Link
Survey, Knee, MRI.
Section, Multiple Entries: 21.8.1 Tomographic Images, CAT, CT, Overviews, Surveys, Datasets Chapter Contents (Back)
Survey, Tomography. Tomography. CT. CAT.
Kak, A.C., and Slaney, M., (Eds.),
Principles of Computerized Tomographic Imaging,
IEEE_PressNew York, 1988.
WWW Link. Survey, Tomography. Buy this book: Principles of Computerized Tomographic Imaging (Classics in Applied Mathematics)
Herman, G.T., Kuba, A., (Eds.),
Advances in Discrete Tomography and Its Applications,
Springer2007. ISBN 978-0-8176-3614-2
WWW Link. Survey, Tomography. Buy this book: Advances in Discrete Tomography and its Applications (Applied and Numerical Harmonic Analysis)
Herman, G.T., Kuba, A.,
Discrete tomography in medical imaging,
PIEEE(91), No. 10, October 2003, pp. 1612-1626.
IEEE DOI
Survey, Tomography.
Cierniak, R.[Robert],
X-Ray Computed Tomography in Biomedical Engineering,
Springer2011, ISBN: 978-0-85729-026-7.
WWW Link. Survey, Tomography. Buy this book: X-Ray Computed Tomography in Biomedical Engineering
Bauer, C., Aurich, V., Arzhaeva, Y., Styner, M.A., van Ginneken, B., Heimann, T., Beichel, R., Chi, Y.[Ying], Cordova, A., Dawant, B.M., Fidrich, M., Furst, J.D., Furukawa, D., Grenacher, L., Hornegger, J., Kainmueller, D., Kitney, R.I., Kobatake, H., Lamecker, H., Lange, T., Lee, J.J.[Jeong-Jin], Lennon, B., Li, R.[Rui], Li, S.[Senhu], Meinzer, H.P., Nemeth, G., Raicu, D.S., Rau, A.M., van Rikxoort, E.M., Rousson, M., Rusko, L., Saddi, K.A., Schmidt, G., Seghers, D., Shimizu, A., Slagmolen, P., Sorantin, E., Soza, G., Susomboon, R., Becker, C., Beck, A., Bekes, G., Bello, F., Binnig, G., Bischof, H., Bornik, A., Cashman, P., Waite, J.M., Wimmer, A., Wolf, I.,
Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets,
MedImg(28), No. 8, August 2009, pp. 1251-1265.
IEEE DOI
Survey, Liver Segmentation. Evaluation, Liver Segmentation.
Rewitt, R.M., and Matej, S.,
Overview of methods for image reconstruction from projections in emission computed tomography,
PIEEE(91), No. 10, October 2003, pp. 1588-1611.
IEEE DOI
Survey, PET.
Tang, Y.[Yunbo], Chen, D.[Dan], Li, X.L.[Xiao-Li],
Dimensionality Reduction Methods for Brain Imaging Data Analysis,
Surveys(54), No. 4, May 2021, pp. xx-yy.
DOI Link
Survey, Brain Imaging. dimensionality reduction, factorization, feature learning, statistical tensor analysis, Brain imaging data, big data
Balafar, M.A., Ramli, A.R., Saripan, M.I., Mashohor, S.,
Review of brain MRI image segmentation methods,
AIR(33), No. 3, March 2010, pp. 261-274.
WWW Link.
Survey, MRI.
Sharma, S.[Shallu], Mandal, P.K.[Pravat Kumar],
A Comprehensive Report on Machine Learning-Based Early Detection of Alzheimer's Disease Using Multi-Modal Neuroimaging Data,
Surveys(55), No. 2, February 2023, pp. xx-yy.
DOI Link
Survey, Alzheimer's. feature scaling, feature fusion, feature selection, Alzheimer disease, machine learning algorithms, multiple modal imaging
Saman, S.[Sangeetha], Narayanan, S.J.[Swathi Jamjala],
Survey on brain tumor segmentation and feature extraction of MR images,
MultInfoRetr(8), No. 2, June 2019, pp. 79-99.
Springer DOI
Survey, Brain Tumors.
Tamilselvan, K.S.[Kumaravel Subramaniam], Murugesan, G.[Govindasamy],
Survey and analysis of various image fusion techniques for clinical CT and MRI images,
IJIST(24), No. 2, 2014, pp. 193-202.
DOI Link
Survey, Fusion. computed tomography
Gholipour, A.[Ali], Kehtarnavaz, N.[Nasser], Briggs, R.W.[Richard W.], Devous, M.[Michael], Gopinath, K.S.[Kaundinya S.],
Brain Functional Localization: A Survey of Image Registration Techniques,
MedImg(26), No. 4, April 2007, pp. 427-451.
IEEE DOI
Survey, Registration.
Lin, Q.A.[Qi-Ang], Man, Z.X.[Zheng-Xing], Cao, Y.C.[Yong-Chun], Deng, T.[Tao], Han, C.C.[Cheng-Cheng], Cao, C.G.[Chuan-Gui], Zhang, L.J.[Lin-Jun], Zeng, S.[Sitao], Gao, R.T.[Rui-Ting], Wang, W.[Weilan], Ji, J.S.[Jin-Shui], Huang, X.D.[Xiao-Di],
Classifying functional nuclear images with convolutional neural networks: A survey,
IET-IPR(14), No. 14, December 2020, pp. 3300-3313.
DOI Link
Survey, Nuclear Imaging.
Sanei, S., Ferdowsi, S., Nazarpour, K., Cichocki, A.,
Advances in Electroencephalography Signal Processing,
SPMag(30), No. 1, 2012, pp. 170-176.
IEEE DOI
Survey, EEG.
Chen, X.[Xun], Li, C.[Chang], Liu, A.[Aiping], McKeown, M.J.[Martin J.], Qian, R.[Ruobing], Wang, Z.J.[Z. Jane],
Toward Open-World Electroencephalogram Decoding Via Deep Learning: A comprehensive survey,
SPMag(39), No. 2, March 2022, pp. 117-134.
IEEE DOI
Survey, EEG. Deep learning, Brain models, Systematics, Semantics, Tutorials,
WWW Link. ction, Electroencephalography, Decoding
Suri, J.S.[Jasjit S.],
Computer Vision, Pattern Recognition and Image Processing in Left Ventricle Segmentation: The Last 50 Years,
PAA(3), No. 3 2000, pp. 209-242.
Survey, Segmentation.
Bi, L.Z.[Lu-Zheng], Fan, X.A.[Xin-An], Liu, Y.[Yili],
EEG-Based Brain-Controlled Mobile Robots: A Survey,
HMS(43), No. 2, March 2013, pp. 161-176.
IEEE DOI
Survey, BCI.
Bablani, A.[Annushree], Edla, D.R.[Damodar Reddy], Tripathi, D.[Diwakar], Cheruku, R.[Ramalingaswamy],
Survey on Brain-Computer Interface: An Emerging Computational Intelligence Paradigm,
Surveys(51), No. 1, February 2019, pp. Article No 20.
DOI Link
Survey, Brain-Computer Interface.
Bernal, S.L.[Sergio Lopez], Celdran, A.H.[Alberto Huertas], Perez, G.M.[Gregorio Martinez], Barros, M.T.[Michael Taynnan], Balasubramaniam, S.[Sasitharan],
Security in Brain-Computer Interfaces: State-of-the-Art, Opportunities, and Future Challenges,
Surveys(54), No. 1, January 2021, pp. xx-yy.
DOI Link
Survey, Brain-Computer Interface. BCI, safety, cybersecurity, privacy, Brain-computer interfaces
Bibiloni, P., González-Hidalgo, M., Massanet, S.,
A survey on curvilinear object segmentation in multiple applications,
PR(60), No. 1, 2016, pp. 949-970.
Elsevier DOI
Survey, Segmentation. Blood vessels, etc. Image processing
McDuff, D.[Daniel],
Camera Measurement of Physiological Vital Signs,
Surveys(55), No. 9, January 2023, pp. xx-yy.
DOI Link
Survey, Photoplethysmography. signal processing, machine learning, Physiology, thermal imaging
Section, Multiple Entries: 21.11.6 MRI, Surveys, Overviews, Evaluations Chapter Contents (Back)
Survey, MRI. Magnetic Resonance. Three-Dimensional Models.
Hornak, J.P.[Joseph P.],
The Basics of MRI,
Online Book1999.
HTML Version. Survey, MRI. A long discussion of the physics and practice of MRI. Look here if you have questions.
Avola, D.[Danilo], Cinque, L.[Luigi], Fagioli, A.[Alessio], Foresti, G.[Gianluca], Mecca, A.[Alessio],
Ultrasound Medical Imaging Techniques: A Survey,
Surveys(54), No. 3, April 2021, pp. xx-yy.
DOI Link
Survey, Ultrasound. computer aided detection, echocardiography, trus, US, abus, Medical image, ultrasound, segmentation, surgeon aid, classification
Noble, J.A., Boukerroui, D.,
Ultrasound Image Segmentation: A Survey,
MedImg(25), No. 8, August 2006, pp. 987-1010.
IEEE DOI
Survey, Ultrasound.
Hu, Z.L.[Zi-Long], Tang, J.S.[Jin-Shan], Wang, Z.M.[Zi-Ming], Zhang, K.[Kai], Zhang, L.[Ling], Sun, Q.L.[Qing-Ling],
Deep learning for image-based cancer detection and diagnosis: A survey,
PR(83), 2018, pp. 134-149.
Elsevier DOI
Survey, Cancer Detection.
Suzuki, K.[Kenji],
Machine Learning in Computer-Aided Diagnosis of the Thorax and Colon in CT: A Survey,
IEICE(E96-D), No. 4, April 2013, pp. 772-783.
WWW Link.
Survey, Thorax. Survey, Colon.
Huang, T.S., and Netravali, A.N.,
Motion and Structure from Feature Correspondences: A Review,
PIEEE(82), No. 2, February 1994, pp. 252-268.
And: AIPU02(331-348). Survey, Motion. Motion, Survey. Perspective assumptions.
Nagel, H.H.,
Image Sequence Evaluation: 30 Years and Still Going Strong,
ICPR00(Vol I: 149-158).
IEEE DOI
Survey, Motion. Survey of motion research
Nagel, H.H.,
Analysis Techniques for Image Sequences,
ICPR78(186-211). Survey, Motion. Motion, Survey. Good bibliography of the earlier papers. An expanded version appears later??
Martin, W.N., and Aggarwal, J.K.,
Dynamic Scene Analysis: A Survey,
CGIP(7), No. 3, June 1978, pp. 356-374.
Elsevier DOI (December 76, April 77, and June 78). Survey, Motion.
And:
Dynamic Scene Analysis,
ISPDSA83(40-73). Seems to be an almost null intersection with other surveys.
Cedras, C., Shah, M.,
Motion-Based Recognition: A Survey,
IVC(13), No. 2, March 1995, pp. 129-155.
Elsevier DOI Survey, Motion. Survey, Moving Light Display.
Earlier:
A Survey of Motion Analysis from Moving Light Displays,
CVPR94(214-221).
IEEE DOI
Aggarwal, J.K., and Nandhakumar, N.,
On the Computation of Motion from Sequences of Images: A Review,
PIEEE(76), 1988, pp. 917-935.
IEEE DOI
Earlier: Univ. of TexasTR-88-2-47, April 1988. Survey, Motion. Motion, Survey.
Åström, K.,
Invited Paper: Multiple View Vision,
ICPR00(Vol I: 59-66).
IEEE DOI
Survey, Motion. Survey, 3D. More on motion than multiple view stereo, but good references to different computational techniques.
Sabata, B.[Bikash], Aggarwal, J.K.,
Estimation of Motion from a Pair of Range Images: A Review,
CVGIP(54), No. 3, November 1991, pp. 309-324.
Elsevier DOI Survey, Motion. Motion, Survey. Discussion of various methods with the same formulation.
See also Segmentation of 3-D Range Images Using Pyramidal Data Structures.
CVonline: Motion, Tracking and Time Sequence Analysis,
CV-OnlineJuly 2001.
HTML Version. Survey, Motion. Survey, Tracking.
Cox, I.J.,
A Review of Statistical Data Association Techniques for Motion Correspondence,
IJCV(10), No. 1, February 1993, pp. 53-66.
Springer DOI Survey, Tracking. Techniques that came from target tracking work.
Jia, Z.[Zhen], Balasuriya, A.[Arjuna], Challa, S.[Subhash],
Vision Based Target Tracking for Autonomous Land Vehicle Navigation: A Brief Survey,
RPCS(2), No. 1, January 2009, pp. 32-42.
WWW Link.
Survey, Tracking.
Oprea, S.[Sergiu], Martinez-Gonzalez, P.[Pablo], Garcia-Garcia, A.[Alberto], Castro-Vargas, J.A.[John Alejandro], Orts-Escolano, S.[Sergio], Garcia-Rodriguez, J.[Jose], Argyros, A.A.[Antonis A.],
A Review on Deep Learning Techniques for Video Prediction,
PAMI(44), No. 6, June 2022, pp. 2806-2826.
IEEE DOI
Survey, Video Prediction. Predictive models, Task analysis, Uncertainty, Deep learning, Computational modeling, Video sequences, Training, self-supervised learning
Wen, J.J.[Jia-Jun], Xu, Y.[Yong], Chen, Y.[Yan], He, L.W.[Li-Wen],
Recent Advance on Mean Shift Tracking: A Survey,
IJIG(13), No. 03, 2013, pp. 1350012.
DOI Link
Survey, Mean Shift.
Arulampalam, S., Maskell, S., Gordon, N., and Clapp, T.,
A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking,
TSP(50), No. 2, 2002, pp. 174-188.
WWW Link.
IEEE DOI Survey, Particle Filters.
Section, Multiple Entries: 16.6.2.16 Target Tracking Techniques, Performance Evaluation, Comparison, Benchmarks, Datasets, Survey Chapter Contents (Back)
Evaluation, Target Tracking. Target Tracking. Survey, Target Tracking.
See also Target Tracking Challenges, Result Summaries.
Nagarajan, V., Chideambara, M.R., and Sharma, R.N.,
Combinatorial Problems in Multitarget Tracking: A Comprehensive Survey,
IEE-P(F: 134), No. 1, 1987, pp. 113-118. Survey, Target Tracking.
Mazor, E., Averbuch, A., Bar-Shalom, Y., Dayan, J.,
Interacting Multiple Model Methods in Target Tracking: A Survey,
AeroSys(34), No. 1, January 1998, pp. 103-123.
Survey, Target Tracking.
Amoozegar, F.,
Neural-Network-Based Target Tracking State-of-the-Art Survey,
OptEng(37), No. 3, March 1998, pp. 836-846.
Survey, Target Tracking.
Lepetit, V.[Vincent], Fua, P.[Pascal],
Monocular Model-Based 3D Tracking of Rigid Objects: A Survey,
FTCGV(1), Issue 1, 2005, pp. 1-89.
DOI Link
Survey, Tracking. Published August 2005.
Yilmaz, A.[Alper], Javed, O.[Omar], Shah, M.[Mubarak],
Object tracking: A survey,
Surveys(38), No. 4 2006, pp. 13.
WWW Link.
Survey, Target Tracking.
Abidi, B.R.[Besma R.], Aragam, N.R.[Nash R.], Yao, Y.[Yi], Abidi, M.A.[Mongi A.],
Survey and analysis of multimodal sensor planning and integration for wide area surveillance,
Surveys(41), No. 1, December 2008, pp. 1-36.
WWW Link.
Survey, Sensor Planning.
Dubuisson, S.[Séverine], Gonzales, C.[Christophe],
A survey of datasets for visual tracking,
MVA(27), No. 1, January 2016, pp. 23-52.
WWW Link.
Survey, Tracking. Dataset, Tracking.
Souza, É.L.[Éfren L.], Nakamura, E.F.[Eduardo F.], Pazzi, R.W.[Richard W.],
Target Tracking for Sensor Networks: A Survey,
Surveys(48), No. 3, February 2016, pp. 30.
DOI Link
Survey, Tracking. Use three different formulations for the target-tracking problem and classify the target-tracking algorithms based on common characteristics. Organize tracking into six components: target detection, node cooperation, position computation, future-position estimation, energy management, and target recovery.
Fiaz, M.[Mustansar], Mahmood, A.[Arif], Javed, S.[Sajid], Jung, S.K.[Soon Ki],
Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends,
Surveys(51), No. 1, February 2019, pp. Article No 43.
DOI Link
Survey, Tracking.
Luo, W.H.[Wen-Han], Xing, J.L.[Jun-Liang], Milan, A.[Anton], Zhang, X.Q.[Xiao-Qin], Liu, W.[Wei], Kim, T.K.[Tae-Kyun],
Multiple object tracking: A literature review,
AI(293), 2021, pp. 103448.
Elsevier DOI
Survey, Tracking. Multi-object tracking, Data association, Survey
Section, Multiple Entries: 16.6.2.16.1 Target Tracking Challenges, Result Summaries Chapter Contents (Back)
Evaluation, Target Tracking. Challenge Results. Survey, Target Tracking.
Section, Multiple Entries: 16.7.1 Surveillance, Human Motion, Surveys, Reviews, Overviews, Representations Chapter Contents (Back)
Survey, Motion, Human. Survey, Surveillance.
Hu, W., Tan, T.N., Wang, L., Maybank, S.J.,
A Survey on Visual Surveillance of Object Motion and Behaviors,
SMC-C(34), No. 3, August 2004, pp. 334-352.
IEEE Abstract.
Survey, Surveillance.
Räty, T.D.,
Survey on Contemporary Remote Surveillance Systems for Public Safety,
SMC-C(40), No. 5, September 2010, pp. 493-515.
IEEE DOI
Survey, Surveillance. State of the art.
Porikli, F.M., Bremond, F., Dockstader, S.L., Ferryman, J.M., Hoogs, A., Lovell, B.C., Pankanti, S., Rinner, B., Tu, P., Venetianer, P.L.,
Video Surveillance: Past, Present, and Now the Future,
SPMag(30), No. 3, 2012, pp. 190-198.
IEEE DOI
Survey, Surveillance. DSP Forum.
Senior, A.W.[Andrew W.],
Protecting Privacy in Video Surveillance,
Springer2009, ISBN: 978-1-84882-300-6.
WWW Link. Survey, Biometrics. Overview of surveillance, issues of privacy. Buy this book: Protecting Privacy in Video Surveillance
Morris, B.T.[Brendan T.], Trivedi, M.M.[Mohan M.],
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance,
CirSysVideo(18), No. 8, August 2008, pp. 1114-1127.
IEEE DOI
Survey, Trajectory Analysis.
Ambardekar, A.[Amol], Nicolescu, M.[Mircea], Bebis, G.N.[George N.], Nicolescu, M.[Monica],
Vehicle classification framework: a comparative study,
JIVP(2014), No. 1, 2014, pp. 29.
DOI Link
Survey, Vehicle Classification.
Yang, Z.[Zi], Pun-Cheng, L.S.C.[Lilian S.C.],
Vehicle detection in intelligent transportation systems and its applications under varying environments: A review,
IVC(69), 2018, pp. 143-154.
Elsevier DOI
Survey, Vehicle Detection. Vehicle detection, Intelligent Transportation Systems, Varying environments, Traffic surveillance
Boukerche, A.[Azzedine], Siddiqui, A.J.[Abdul Jabbar], Mammeri, A.[Abdelhamid],
Automated Vehicle Detection and Classification: Models, Methods, and Techniques,
Surveys(50), No. 5, November 2017, pp. Article No 62.
DOI Link
Survey, Vehicle Detection. Categorize based on granularity of recognition.
Kastrinaki, V., Zervakis, M., Kalaitzakis, K.,
A survey of video processing techniques for traffic applications,
IVC(21), No. 4, April 2003, pp. 359-381.
Elsevier DOI
Survey, Traffic.
Lecky, N.[Ned],
Machine Vision Gets Moving: Part I,
VisSys(16), No. 9, September 2011, pp. 17-19.
And:
Machine Vision Gets Moving: Part II,
VisSys(16), No. 10, October 2011, pp. xx-yy.
HTML Version.
And:
Machine Vision Gets Moving: Part III,
VisSys(16), No. 11, November 2011, pp. 7-11. Survey, Traffic. Trade level survey of what is happeing in the field.
Buch, N., Velastin, S.A., Orwell, J.,
A Review of Computer Vision Techniques for the Analysis of Urban Traffic,
ITS(12), No. 3, September 2011, pp. 920-939.
IEEE DOI
Survey, Traffic Analysis.
Aleksander, R.[Rydzewski], Pawel, C.[Czarnul],
Recent advances in traffic optimisation: systematic literature review of modern models, methods and algorithms,
IET-ITS(14), No. 13, 15 December 2020, pp. 1740-1758.
DOI Link
Survey, Traffic.
Khan, S.D.[Sultan Daud], Ullah, H.[Habib],
A survey of advances in vision-based vehicle re-identification,
CVIU(182), 2019, pp. 50-63.
Elsevier DOI
Survey, Vehicle Re-Identification. Re-identification, Hand-crafted methods, Convolutional neural network, Traffic analysis
Zepf, S.[Sebastian], Hernandez, J.[Javier], Schmitt, A.[Alexander], Minker, W.[Wolfgang], Picard, R.W.[Rosalind W.],
Driver Emotion Recognition for Intelligent Vehicles: A Survey,
Surveys(53), No. 3, June 2020, pp. xx-yy.
DOI Link
Survey, Driver Monitoring. machine learning, emotion measurement, literature survey, Affective computing, road safety, intelligent user sensing
Datondji, S.R.E., Dupuis, Y., Subirats, P., Vasseur, P.,
A Survey of Vision-Based Traffic Monitoring of Road Intersections,
ITS(17), No. 10, October 2016, pp. 2681-2698.
IEEE DOI
Survey, Traffic. Accidents
Kalakanti, A.K.[Arun Kumar], Rao, S.[Shrisha],
Computational Challenges and Approaches for Electric Vehicles,
Surveys(55), No. 14s, July 2023, pp. xx-yy.
DOI Link
Survey, Electric Vehicles. electric vehicle charge scheduling, electric vehicle routing problem, Electric vehicles, charging station size
Wang, T.[Tian], Liang, Y.Z.[Yu-Zhu], Shen, X.W.[Xue-Wei], Zheng, X.[Xi], Mahmood, A.[Adnan], Sheng, Q.Z.[Quan Z.],
Edge Computing and Sensor-Cloud: Overview, Solutions, and Directions,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link
Survey, Edge Computing. edge computing, Sensor-cloud, WSNs, cloud computing
Ning, Z.L.[Zhao-Long], Hu, H.[Hao], Wang, X.J.[Xiao-Jie], Guo, L.[Lei], Guo, S.[Song], Wang, G.[Guoyin], Gao, X.B.[Xin-Bo],
Mobile Edge Computing and Machine Learning in the Internet of Unmanned Aerial Vehicles: A Survey,
Surveys(56), No. 1, August 2023, pp. 13.
DOI Link
Survey, Edge Computing. The Internet of unmanned aerial vehicles, computation offloading, intelligent decision making, mobile edge computing
Lu, Z., Qu, G., Liu, Z.,
A Survey on Recent Advances in Vehicular Network Security, Trust, and Privacy,
ITS(20), No. 2, February 2019, pp. 760-776.
IEEE DOI
Survey, VANET Security. Privacy, Authentication, Vehicular ad hoc networks, Safety, Tools, VANETs, security, privacy, trust management, simulation tools
Suo, D.J.[Da-Jiang], Moore, J.[John], Boesch, M.[Mathew], Post, K.[Kyle], Sarma, S.E.[Sanjay E.],
Location-Based Schemes for Mitigating Cyber Threats on Connected and Automated Vehicles: A Survey and Design Framework,
ITS(23), No. 4, April 2022, pp. 2919-2937.
IEEE DOI
Survey, VANET Security. Security, Safety, Transportation, Sensors, Roads, Privacy, Real-time systems, Connected and automated vehicles, security, V2I
Agbaje, P.[Paul], Anjum, A.[Afia], Mitra, A.[Arkajyoti], Oseghale, E.[Emmanuel], Bloom, G.[Gedare], Olufowobi, H.[Habeeb],
Survey of Interoperability Challenges in the Internet of Vehicles,
ITS(23), No. 12, December 2022, pp. 22838-22861.
IEEE DOI
Survey, VANET. Interoperability, Ecosystems, Standards, Real-time systems, Vehicle dynamics, Semantics, Internet of Vehicles, intelligent transportation system
Wu, H.[Hao],
A Survey of Battery Swapping Stations for Electric Vehicles: Operation Modes and Decision Scenarios,
ITS(23), No. 8, August 2022, pp. 10163-10185.
IEEE DOI
Survey, Battery Swapping. Batteries, Vehicles, Load modeling, Electric vehicle charging, Costs, Statistics, Sociology, Battery swapping stations, transportation
Zhang, J., Wang, F.Y., Wang, K., Lin, W.H., Xu, X., Chen, C.,
Data-Driven Intelligent Transportation Systems: A Survey,
ITS(12), No. 4, December 2011, pp. 1624-1639.
IEEE DOI
Survey, Transportation Systems. Sardis Award, Survey.
Moral-Muñoz, J.A., Cobo, M.J., Chiclana, F., Collop, A., Herrera-Viedma, E.,
Analyzing Highly Cited Papers in Intelligent Transportation Systems,
ITS(17), No. 4, April 2016, pp. 993-1001.
IEEE DOI
Survey, Intelligent Transportation.
Zhao, D., Dai, Y., Zhang, Z.,
Computational Intelligence in Urban Traffic Signal Control: A Survey,
SMC-C(42), No. 4, July 2012, pp. 485-494.
IEEE DOI
Survey, Traffic Signals.
Silva, F.A.[Fabrício A.], Boukerche, A.[Azzedine], Silva, T.R.M.B.[Thais R. M. Braga], Ruiz, L.B.[Linnyer B.], Cerqueira, E.[Eduardo], Loureiro, A.A.F.[Antonio A. F.],
Vehicular Networks: A New Challenge for Content-Delivery-Based Applications,
Surveys(49), No. 1, July 2016, pp. Article No 11.
DOI Link
Survey, Vehicle Networks. A significant number of promising applications for vehicular ad hoc networks (VANETs) are becoming a reality. Most of these applications require a variety of heterogenous content to be delivered to vehicles and to their on-board users.
Tuohy, S., Glavin, M., Hughes, C., Jones, E., Trivedi, M., Kilmartin, L.,
Intra-Vehicle Networks: A Review,
ITS(16), No. 2, April 2015, pp. 534-545.
IEEE DOI
Survey, Vehicle Networks. Automotive engineering
Boukerche, A.[Azzedine], Magnano, A.[Alexander], Aljeri, N.[Noura],
Mobile IP Handover for Vehicular Networks: Methods, Models, and Classifications,
Surveys(49), No. 4, February 2017, pp. Article No 73.
DOI Link
Survey, Vehicle Networks.
Ahmed, E., Gharavi, H.,
Cooperative Vehicular Networking: A Survey,
ITS(19), No. 3, March 2018, pp. 996-1014.
IEEE DOI
Survey, VANETS. Ad hoc networks, Cooperative communication, MIMO communication, Media Access Protocol, Relays, Spatial diversity, vehicular communication
Celes, C.[Clayson], Boukerche, A.[Azzedine], Loureiro, A.A.F.[Antonio A. F.],
Mobility Trace Analysis for Intelligent Vehicular Networks: Methods, Models, and Applications,
Surveys(54), No. 3, April 2021, pp. xx-yy.
DOI Link
Survey, VANET. survey, Vehicular networks, topology, data mining, data analysis, vanet, mobility, routing
Skog, I., Handel, P.,
In-Car Positioning and Navigation Technologies: A Survey,
ITS(10), No. 1, March 2009, pp. 4-21.
IEEE DOI
Survey, Driver Monitoring.
Metaxas, D.N.[Dimitris N.], Zhang, S.T.[Shao-Ting],
A review of motion analysis methods for human Nonverbal Communication Computing,
IVC(31), No. 6-7, June-July 2013, pp. 421-433.
Elsevier DOI
Survey, Facial Expressions. Nonverbal Communication Computing; Motion analysis; Face tracking; Facial expression recognition; Gesture recognition; Group activity analysis
Anagnostopoulos, C.N.E., Anagnostopoulos, I.E., Psoroulas, I.D., Loumos, V., Kayafas, E.,
License Plate Recognition From Still Images and Video Sequences: A Survey,
ITS(9), No. 3, September 2008, pp. 377-391.
IEEE DOI
Survey, License Plates.
Rastgoo, M.N.[Mohammad Naim], Nakisa, B.[Bahareh], Rakotonirainy, A.[Andry], Chandran, V.[Vinod], Tjondronegoro, D.[Dian],
A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements,
Surveys(51), No. 5, January 2019, pp. Article No 88.
DOI Link
Survey, Driver Stress.
Sikander, G., Anwar, S.,
Driver Fatigue Detection Systems: A Review,
ITS(20), No. 6, June 2019, pp. 2339-2352.
IEEE DOI
Survey, Driver Fatigue. Fatigue, Vehicles, Sleep, Monitoring, Mathematical model, Feature extraction, Task analysis, Intelligent transportation, driver monitoring
Nemcová, A.[Andrea], Svozilová, V.[Veronika], Bucsuházy, K.[Katerina], Smíšek, R.[Radovan], Mézl, M.[Martin], Hesko, B.[Branislav], Belák, M.[Michal], Bilík, M.[Martin], Maxera, P.[Pavel], Seitl, M.[Martin], Dominik, T.[Tomáš], Semela, M.[Marek], Šucha, M.[Matúš], Kolár, R.[Radim],
Multimodal Features for Detection of Driver Stress and Fatigue: Review,
ITS(22), No. 6, June 2021, pp. 3214-3233.
IEEE DOI
Survey, Driver Monitoring. Stress, Fatigue, Databases, Accidents, Automobiles, Sleep, Driver fatigue, driver stress, traffic accident, multimodal features
Section, Multiple Entries: 17.1.1 Human Motion, Surveys, Reviews, Overviews, Representations Chapter Contents (Back)
Survey, Human Motions.
Badler, N.I., and Smoliar, S.W.,
Digital Representations of Human Movement,
Surveys(11), No. 1, March 1979, pp. 19-38. Survey, Motion, Human.
Gavrila, D.M.[Dariu M.],
The Visual Analysis of Human Movement: A Survey,
CVIU(73), No. 1, January 1999, pp. 82-98.
DOI Link
PDF File. Survey, Human Motion.
Chellappa, R.[Rama], Roy-Chowdhury, A.K.[Amit K.], Zhou, S.H.K.[Shao-Hua Kevin],
Recognition of Humans and Their Activities Using Video,
Morgan Claypool2005. Synthesis Lectures on Image, Video, and Multimedia Processing Survey, Activity Recognition.
DOI Link
Turaga, P.K., Chellappa, R., Subrahmanian, V.S., Udrea, O.,
Machine Recognition of Human Activities: A Survey,
CirSysVideo(18), No. 11, November 2008, pp. 1473-1488.
IEEE DOI
Survey, Activity Recognition.
Weinland, D.[Daniel], Ronfard, R.[Remi], Boyer, E.[Edmond],
A Survey of Vision-Based Methods for Action Representation, Segmentation and Recognition,
CVIU(115), No. 2, February 2011, pp. 224-241.
Elsevier DOI
Survey, Activity Recognition. Award, CVIU, Most Cited. (2010-2012) Action/activity recognition; Survey; Computer vision
Chaquet, J.M.[Jose M.], Carmona, E.J.[Enrique J.], Fernandez-Caballero, A.[Antonio],
A survey of video datasets for human action and activity recognition,
CVIU(117), No. 6, June 2013, pp. 633-659.
Elsevier DOI
Survey, Activity Recognition. Dataset, Activity Recognition. Human action recognition; Human activity recognition; Database; Dataset; Review; Survey
Hassner, T.[Tal],
A Critical Review of Action Recognition Benchmarks,
ActionSim13(245-250)
IEEE DOI
Survey, Action Recogniton.
Parent, C.[Christine], Spaccapietra, S.[Stefano], Renso, C.[Chiara], Andrienko, G.[Gennady], Andrienko, N.[Natalia], Bogorny, V.[Vania], Damiani, M.L.[Maria Luisa], Gkoulalas-Divanis, A.[Aris], Macedo, J.[Jose], Pelekis, N.[Nikos], Theodoridis, Y.[Yannis], Yan, Z.X.[Zhi-Xian],
Semantic trajectories modeling and analysis,
Surveys(45), No. 2, February 2013, pp. Article No 42.
DOI Link
Survey, Trajectory Analysis. Focus on movement data has increased as a consequence of the larger availability of such data due to current GPS, GSM, RFID, and sensors techniques. In parallel, interest in movement has shifted from raw movement data analysis to more application-oriented
Wang, S.[Sheng], Bao, Z.F.[Zhi-Feng], Culpepper, J.S.[J. Shane], Cong, G.[Gao],
A Survey on Trajectory Data Management, Analytics, and Learning,
Surveys(54), No. 2, March 2021, pp. xx-yy.
DOI Link
Survey, Trajectory Analysis. similarity search, storage system, deep learning, urban analytics, Trajectory
Popoola, O.P., Wang, K.,
Video-Based Abnormal Human Behavior Recognition: A Review,
SMC-C(42), No. 6, November 2012, pp. 865-878.
IEEE DOI
Survey, Human Activity.
Wang, J.J.L.[Jessica Jun-Lin], Singh, S.[Sameer],
Video analysis of human dynamics: A Survey,
RealTimeImg(9), No. 5, October 2003, pp. 320-345.
Elsevier DOI
Survey, Human Motion.
Moeslund, T.B.[Thomas B.], Granum, E.[Erik],
A Survey of Computer Vision-Based Human Motion Capture,
CVIU(81), No. 3, March 2001, pp. 231-268.
DOI Link
Survey, Motion Capture. Gesture Recognition.
Earlier:
Multiple Cues for Model-Based Human Motion Capture,
AFGR00(362-367).
IEEE DOI
Moeslund, T.B.[Thomas B.], Hilton, A.[Adrian], Krüger, V.[Volker],
A survey of advances in vision-based human motion capture and analysis,
CVIU(103), No. 2-3, November-December 2006, pp. 90-126.
Elsevier DOI
Survey, Motion Capture. Review; Human motion; Initialization; Tracking; Pose estimation; Recognition
Zhan, B.B.[Bei-Bei], Monekosso, D.N.[Dorothy N.], Remagnino, P.[Paolo], Velastin, S.A.[Sergio A.], Xu, L.Q.[Li-Qun],
Crowd analysis: a survey,
MVA(19), No. 5-6, October 2008, pp. xx-yy.
Springer DOI
Survey, Motion, Human.
Earlier: A1, A3, A4, A2, A5:
Motion Estimation with Edge Continuity Constraint for Crowd Scene Analysis,
ISVC06(II: 861-869).
Springer DOI
Candamo, J., Shreve, M., Goldgof, D.B., Sapper, D.B., Kasturi, R.,
Understanding Transit Scenes: A Survey on Human Behavior-Recognition Algorithms,
ITS(11), No. 1, March 2010, pp. 206-224.
IEEE DOI
Survey, Human Detection.
Ji, X.F.[Xiao-Fei], Liu, H.H.[Hong-Hai],
Advances in View-Invariant Human Motion Analysis: A Review,
SMC-C(40), No. 1, January 2010, pp. 13-24.
IEEE DOI
Survey, Human Motion. Motion, Human.
Poppe, R.[Ronald],
A survey on vision-based human action recognition,
IVC(28), No. 6, June 2010, pp. 976-990.
Elsevier DOI
Survey, Action Recognition. Award, IVC Most Cited. (for 2009-2011) Award, IVC Most Cited. (for 2010-2012) Human action recognition; Motion analysis; Action detection
Aggarwal, J.K., Ryoo, M.S.,
Human Activity Analysis: A Review,
Surveys(43), No. 3, April 2011, pp. Article No 16.
DOI Link
Survey, Human Activity. Surveillance systems, patient monitoring systems, human-computer interfaces. Overview of state-of-the-art research papers on human activity recognition. Both simple human actions and high-level activities.
del Rose, M.S.[Michael S.], and Wagner, C.C.[Christian C.],
Survey on classifying human actions through visual sensors,
AIR(37), No. 4, 2012, pp. 301-311.
Springer DOI Survey, Human Actions. Human level performance is not there, efforts focus on parts of the problem. Survey approaches that rely mainly on understanding the image processing and classification of a limited number of actions.
Jiang, Y.G.[Yu-Gang], Bhattacharya, S.[Subhabrata], Chang, S.F.[Shih-Fu], Shah, M.[Mubarak],
High-level event recognition in unconstrained videos,
MultInfoRetr(2), No. 2, June 2013, pp. 73-101.
Springer DOI
PDF File.
Survey, Event Recognition.
Kleinsmith, A.[Andrea], Bianchi-Berthouze, N.[Nadia],
Affective Body Expression Perception and Recognition: A Survey,
AffCom(4), No. 1, January 2013, pp. 15-33.
IEEE DOI
Survey, Action Recognition.
Lim, C.H.[Chern Hong], Vats, E.[Ekta], Chan, C.S.[Chee Seng],
Fuzzy human motion analysis: A review,
PR(48), No. 5, 2015, pp. 1773-1796.
Elsevier DOI
Survey, Human Motion. Human motion analysis
Ziaeefard, M.[Maryam], Bergevin, R.[Robert],
Semantic human activity recognition: A literature review,
PR(48), No. 8, 2015, pp. 2329-2345.
Elsevier DOI
Survey, Activity Recogniton. Human activity recognition
Abdallah, Z.S.[Zahraa S.], Gaber, M.M.[Mohamed Medhat], Srinivasan, B.[Bala], Krishnaswamy, S.[Shonali],
Activity Recognition with Evolving Data Streams: A Review,
Surveys(51), No. 4, August 2018, pp. Article No 71.
DOI Link
Survey, Activity Recognition. From a variety of sensor data.
Scheunert, U., Cramer, H., Fardi, B., Wanielik, G.,
Multi Sensor Based Tracking of Pedestrians: A Survey of Suitable Movement Models,
IVS04(774-778).
IEEE DOI
Survey, Motion, Human.
Geronimo, D.[David], Lopez, A.M.[Antonio M.], Sappa, A.D.[Angel D.], Graf, T.[Thorsten],
Survey of Pedestrian Detection for Advanced Driver Assistance Systems,
PAMI(32), No. 7, July 2010, pp. 1239-1258.
IEEE DOI
Survey, Pedestrian Detection. Driver Assistance. How to deal with the variations in appearance of pedestrians.
Section, Multiple Entries: 17.1.3.2.8 Surveys, Evaluation, Datasets, Human Detection, People Detection, Pedestrians Chapter Contents (Back)
Human Detection. Evaluation, Pedestrian Detection. Survey, Pedestrian Detection.
See also Human Detection, People Detection, Pedestrians, Locating.
See also Tracking People, Human Tracking, Pedestrian Tracking.
Enzweiler, M.[Markus], Gavrila, D.M.[Dariu M.],
Monocular Pedestrian Detection: Survey and Experiments,
PAMI(31), No. 12, December 2009, pp. 2179-2195.
IEEE DOI
Survey, Pedestrian Detection.
See also Daimler Pedestrian Detection Benchmark. wavelet-based AdaBoost cascade (
See also Detecting Pedestrians Using Patterns of Motion and Appearance. ), HOG/linSVM (
See also Histograms of Oriented Gradients for Human Detection. ), NN/LRF (
See also Adaptable Time-Delay Neural Network Algorithm for Image Sequence Analysis, An. ), and combined shape-texture detection (
See also Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle. )
Gandhi, T.[Tarak], Trivedi, M.M.[Mohan Manubhai],
Pedestrian Protection Systems: Issues, Survey, and Challenges,
ITS(8), No. 3, September 2007, pp. 413-430.
IEEE DOI
Survey, Pedestrian Detection.
Simonnet, D., Velastin, S.A.[Sergio A.], Turkbeyler, E., Orwell, J.,
Backgroundless detection of pedestrians in cluttered conditions based on monocular images: a review,
IET-CV(6), No. 6, 2012, pp. 540-550.
DOI Link
Survey, Pedestrian Detection.
Zhang, S., Benenson, R.[Rodrigo], Omran, M.[Mohamed], Hosang, J.[Jan], Schiele, B.[Bernt],
Towards Reaching Human Performance in Pedestrian Detection,
PAMI(40), No. 4, April 2018, pp. 973-986.
IEEE DOI
Survey, Pedestrian Detection.
Earlier:
How Far are We from Solving Pedestrian Detection?,
CVPR16(1259-1267)
IEEE DOI
Earlier: A2, A3, A4, A5, Only:
Ten Years of Pedestrian Detection, What Have We Learned?,
CVRoads14(613-627).
Springer DOI
convolution, neural nets, object detection, pedestrians, Caltech pedestrian dataset, integral channel features
Xiao, Y.Q.[Yan-Qiu], Zhou, K.[Kun], Cui, G.Z.[Guang-Zhen], Jia, L.H.[Lian-Hui], Fang, Z.P.[Zhan-Peng], Yang, X.C.[Xian-Chao], Xia, Q.P.[Qiong-Pei],
Deep learning for occluded and multi-scale pedestrian detection: A review,
IET-IPR(15), No. 2, 2021, pp. 286-301.
DOI Link
Survey, Pedestrian Detection.
Hurney, P., Waldron, P., Morgan, F., Jones, E., Glavin, M.,
Review of pedestrian detection techniques in automotive far-infrared video,
IET-ITS(9), No. 8, 2015, pp. 824-832.
DOI Link
Survey, Pedestrain Detection. driver information systems
Rasouli, A., Tsotsos, J.K.,
Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice,
ITS(21), No. 3, March 2020, pp. 900-918.
IEEE DOI
Survey, Pedestrian Detection. Autonomous vehicles, Roads, Cameras, Automobiles, Observers, pedestrian behavior, traffic interaction, survey
Ming, Z.Q.[Zhang-Qiang], Zhu, M.[Min], Wang, X.K.[Xiang-Kun], Zhu, J.[Jiamin], Cheng, J.L.[Jun-Long], Gao, C.R.[Cheng-Rui], Yang, Y.[Yong], Wei, X.Y.[Xiao-Yong],
Deep learning-based person re-identification methods: A survey and outlook of recent works,
IVC(119), 2022, pp. 104394.
Elsevier DOI
Survey, Re-Identification. Person re-identification, Deep metric learning, Local feature learning, Generative adversarial learning, Sequence feature learning
Ye, M.[Mang], Shen, J.B.[Jian-Bing], Lin, G.J.[Gao-Jie], Xiang, T.[Tao], Shao, L.[Ling], Hoi, S.C.H.[Steven C. H.],
Deep Learning for Person Re-Identification: A Survey and Outlook,
PAMI(44), No. 6, June 2022, pp. 2872-2893.
IEEE DOI
Survey, Re-Identification. Annotations, Cameras, Training, Training data, Feature extraction, Data models, Deep learning, Person re-identification, deep learning
Sun, Z.H.[Zhi-Hong], Chen, J.[Jun], Chao, L.[Liang], Ruan, W.J.[Wei-Jian], Mukherjee, M.[Mithun],
A Survey of Multiple Pedestrian Tracking Based on Tracking-by-Detection Framework,
CirSysVideo(31), No. 5, 2021, pp. 1819-1833.
IEEE DOI
Survey, Pedestrian Tracking.
Bedagkar-Gala, A.[Apurva], Shah, S.K.[Shishir K.],
A survey of approaches and trends in person re-identification,
IVC(32), No. 4, 2014, pp. 270-286.
Elsevier DOI
Survey, Re-Identification. Person re-identification
Saghafi, M.A., Hussain, A., Zaman, H.B., Saad, M.H.M.[M.H. Md],
Review of person re-identification techniques,
IET-CV(8), No. 6, 2014, pp. 455-474.
DOI Link
Survey, Re-Identification. feature extraction
Leng, Q., Ye, M., Tian, Q.,
A Survey of Open-World Person Re-Identification,
CirSysVideo(30), No. 4, April 2020, pp. 1092-1108.
IEEE DOI
Survey, Re-Identification. Cameras, Probes, Task analysis, Measurement, Market research, Benchmark testing, Feature extraction, Person re-identification, specific application-driven
Islam, K.[Khawar],
Person search: New paradigm of person re-identification: A survey and outlook of recent works,
IVC(101), 2020, pp. 103970.
Elsevier DOI
Survey, Re-Identification. Person re-identification, Person search, Literature survey, Metric learning, Loss functions
Yaghoubi, E.[Ehsan], Kumar, A.[Aruna], Proença, H.[Hugo],
SSS-PR: A short survey of surveys in person re-identification,
PRL(143), 2021, pp. 50-57.
Elsevier DOI
Survey, Re-Identification. Person re-identification, Privacy and security, Visual surveillance
Behera, N.K.S.[Nayan Kumar Subhashis], Sa, P.K.[Pankaj Kumar], Bakshi, S.[Sambit], Padhy, R.P.[Ram Prasad],
Person re-identification: A taxonomic survey and the path ahead,
IVC(122), 2022, pp. 104432.
Elsevier DOI
Survey, Re-Identification. Person re-identification, Visual surveillance, Computer vision
Zahra, A.[Asmat], Perwaiz, N.[Nazia], Shahzad, M.[Muhammad], Fraz, M.M.[Muhammad Moazam],
Person re-identification: A retrospective on domain specific open challenges and future trends,
PR(142), 2023, pp. 109669.
Elsevier DOI
Survey, Re-Identification. Person re-Identification, Literature survey, Deep learning, Open challenges, Specific application-driven
Deng, T.H.[Teng-Hao], Sun, Y.[Yan],
Recent advances in deterministic human motion prediction: A review,
IVC(143), 2024, pp. 104926.
Elsevier DOI
Survey, Human Motion. Survey, Human motion prediction, Deep learning
Narayan, N.[Neeti], Sankaran, N.[Nishant], Setlur, S.[Srirangaraj], Govindaraju, V.[Venu],
Learning deep features for online person tracking using non-overlapping cameras: A survey,
IVC(89), 2019, pp. 222-235.
Elsevier DOI
Survey, Re-Identification. Online person tracking, Re-identification, Surveillance, Deep features, Recurrent neural network
Rida, I.[Imad], Almaadeed, N.[Noor], Almaadeed, S.[Somaya],
Robust gait recognition: a comprehensive survey,
IET-Bio(8), No. 1, January 2019, pp. 14-28.
DOI Link
Survey, Gait Analysis.
de Marsico, M.[Maria], Mecca, A.[Alessio],
A Survey on Gait Recognition via Wearable Sensors,
Surveys(52), No. 4, September 2019, pp. Article No 86.
DOI Link
Survey, Gait.
Kusakunniran, W.[Worapan],
Review of gait recognition approaches and their challenges on view changes,
IET-Bio(9), No. 6, November 2020, pp. 238-250.
DOI Link
Survey, Gait.
Amsaprabhaa, M., Nancy Jane, Y., Khanna Nehemiah, H.,
A survey on spatio-temporal framework for kinematic gait analysis in RGB videos,
JVCIR(79), 2021, pp. 103218.
Elsevier DOI
Survey, Gait. Human gait recognition, Spatio-temporal features, Gait databases, Gait recognition representation, Gait prediction
Nixon, M.S., Carter, J.N.,
Automatic Recognition by Gait,
PIEEE(94), No. 11, November 2006, pp. 2013-2024.
IEEE DOI
Survey, Gait.
Wan, C.S.[Chang-Sheng], Wang, L.[Li], Phoha, V.V.[Vir V.],
A Survey on Gait Recognition,
Surveys(51), No. 5, January 2019, pp. Article No 89.
DOI Link
Survey, Gait Recognition. Recognition by gait.
Nambiar, A.[Athira], Bernardino, A.[Alexandre], Nascimento, J.C.[Jacinto C.],
Gait-based Person Re-identification: A Survey,
Surveys(51), No. 1, February 2019, pp. Article No 33.
DOI Link
Survey, Gait. Survey, Re-Identification.
Goncalves dos Santos, C.F.[Claudio Filipi], de Souza Oliveira, D.[Diego], Passos, L.A.[Leandro A.], Goncalves-Pires, R.[Rafael], Silva-Santos, D.F.[Daniel Felipe], Valem, L.P.[Lucas Pascotti], Moreira, T.P.[Thierry P.], Santana, M.C.S.[Marcos Cleison S.], Roder, M.[Mateus], Papa, J.P.[Jo Paulo], Colombo, D.[Danilo],
Gait Recognition Based on Deep Learning: A Survey,
Surveys(55), No. 2, February 2023, pp. xx-yy.
DOI Link
Survey, Gait. biometrics, deep learning, Gait recognition
Sepas-Moghaddam, A.[Alireza], Etemad, A.[Ali],
Deep Gait Recognition: A Survey,
PAMI(45), No. 1, January 2023, pp. 264-284.
IEEE DOI
Survey, Gait. Gait recognition, Protocols, Deep learning, Training, Taxonomy, Probes, Market research, Gait recognition, deep learning, gait datasets, feature representation
Tsao, L., Li, L., Ma, L.,
Human Work and Status Evaluation Based on Wearable Sensors in Human Factors and Ergonomics: A Review,
HMS(49), No. 1, February 2019, pp. 72-84.
IEEE DOI
Survey, Ergonomics. Wearable sensors, Biomedical monitoring, Man-machine systems, Data collection, Sensor systems, Human factors, Ergonomics, wearable systems
Liu, M.[Meng], Nie, L.Q.[Li-Qiang], Wang, Y.[Yunxiao], Wang, M.[Meng], Rui, Y.[Yong],
A Survey on Video Moment Localization,
Surveys(55), No. 9, January 2023, pp. xx-yy.
DOI Link
Survey, Action Localization. vision and language, survey, cross-modal retrieval, video moment retrieval, Video moment localization
Gu, F.Q.[Fu-Qiang], Chung, M.H.[Mu-Huan], Chignell, M.[Mark], Valaee, S.[Shahrokh], Zhou, B.[Baoding], Liu, X.[Xue],
A Survey on Deep Learning for Human Activity Recognition,
Surveys(54), No. 8, October 2021, pp. xx-yy.
DOI Link
Survey, Human Activity. deep models, deep learning, Machine learning, mobile sensing, activity recognition
Betancourt, A.[Alejandro], Morerio, P., Regazzoni, C.S., Rauterberg, M.,
The Evolution of First Person Vision Methods: A Survey,
CirSysVideo(25), No. 5, May 2015, pp. 744-760.
IEEE DOI
Survey, Egocentric. Cameras
del Molino, A.G., Tan, C., Lim, J.H., Tan, A.H.,
Summarization of Egocentric Videos: A Comprehensive Survey,
HMS(47), No. 1, February 2017, pp. 65-76.
IEEE DOI
Survey, Egocentric Analysis. image segmentation
Rodin, I.[Ivan], Furnari, A.[Antonino], Mavroeidis, D.[Dimitrios], Farinella, G.M.[Giovanni Maria],
Predicting the future from first person (egocentric) vision: A survey,
CVIU(211), 2021, pp. 103252.
Elsevier DOI
Survey, Egocentric Video. First person vision, Egocentric vision, Future prediction, Anticipation
Ahad, M.A.R.[M. Atiqur Rahman],
Motion History Images for Action Recognition and Understanding,
 
|
WWW Link.
Survey, Motion History Image. Motion history image (MHI) method.
Benmansour, A.[Asma], Bouchachia, A.[Abdelhamid], Feham, M.[Mohammed],
Multioccupant Activity Recognition in Pervasive Smart Home Environments,
Surveys(48), No. 3, February 2016, pp. Article No 34.
DOI Link
Survey, Smart Home. Human activity recognition in ambient intelligent environments like homes, offices, and classrooms has been the center of a lot of research for many years now. The aim is to recognize the sequence of actions by a specific person using sensor readings
Song, L.C.[Liang-Chen], Yu, G.[Gang], Yuan, J.S.[Jun-Song], Liu, Z.C.[Zi-Cheng],
Human pose estimation and its application to action recognition: A survey,
JVCIR(76), 2021, pp. 103055.
Elsevier DOI
Survey, Human Pose. Pose estimation, Action recognition
Guo, G.D.[Guo-Dong], Lai, A.[Alice],
A survey on still image based human action recognition,
PR(47), No. 10, 2014, pp. 3343-3361.
Elsevier DOI
Survey, Action Recognition. Action recognition
Vishwakarma, S.[Sarvesh], Agrawal, A.[Anupam],
A survey on activity recognition and behavior understanding in video surveillance,
VC(29), No. 10, October 2013, pp. 983-1009.
Springer DOI
Survey, Activity Recognition.
And:
Framework for human action recognition using spatial temporal based cuboids,
ICIIP11(1-6).
IEEE DOI
Borges, P.V.K., Conci, N., Cavallaro, A.,
Video-Based Human Behavior Understanding: A Survey,
CirSysVideo(23), No. 11, 2013, pp. 1993-2008.
IEEE DOI
Survey, Activity Recognition. behavioural sciences computing
Bulling, A.[Andreas], Blanke, U.[Ulf], Schiele, B.[Bernt],
A tutorial on human activity recognition using body-worn inertial sensors,
Surveys(46), No. 3, February 2014, pp. Article No 33.
DOI Link
Survey, Activity Recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition.
Rodríguez, N.D.[Natalia Díaz], Cuéllar, M.P., Lilius, J.[Johan], Calvo-Flores, M.D.[Miguel Delgado],
A survey on ontologies for human behavior recognition,
Surveys(46), No. 4, March 2014, pp. Article No 43.
DOI Link
Survey, Activity Recognition. Describing user activity plays an essential role in ambient intelligence. In this work, we review different methods for human activity recognition, classified as data-driven and knowledge-based techniques.
Herath, S.[Samitha], Harandi, M.[Mehrtash], Porikli, F.M.[Fatih M.],
Going deeper into action recognition: A survey,
IVC(60), No. 1, 2017, pp. 4-21.
Elsevier DOI
Survey, Action Recognition. Human action recognition
Koohzadi, M.[Maryam], Charkari, N.M.[Nasrollah Moghadam],
Survey on deep learning methods in human action recognition,
IET-CV(11), No. 8, December 2017, pp. 623-632.
DOI Link
Survey, Action Recognition.
Abu-Bakar, S.A.R.[Syed A.R.],
Advances in human action recognition: an updated survey,
IET-IPR(13), No. 13, November 2019, pp. 2381-2394.
DOI Link
Survey, Action Recognition.
Kong, Y.[Yu], Fu, Y.[Yun],
Human Action Recognition and Prediction: A Survey,
IJCV(130), No. 5, May 2022, pp. 1366-1401.
Springer DOI
Survey, Action Recognition.
Sun, Z.[Zehua], Ke, Q.H.[Qiu-Hong], Rahmani, H.[Hossein], Bennamoun, M.[Mohammed], Wang, G.[Gang], Liu, J.[Jun],
Human Action Recognition from Various Data Modalities: A Review,
PAMI(45), No. 3, March 2023, pp. 3200-3225.
IEEE DOI
Survey, Action Recognition. Feature extraction, Visualization, Skeleton, Optical imaging, Deep learning, Radar, Human action recognition, deep learning, multi-modality
Batalla, J.M.[Jordi Mongay], Vasilakos, A.[Athanasios], Gajewski, M.[Mariusz],
Secure Smart Homes: Opportunities and Challenges,
Surveys(50), No. 5, November 2017, pp. Article No 75.
DOI Link
Survey, Smart Homes. Sensors, security, future.
Nathan, V., Paul, S., Prioleau, T., Niu, L., Mortazavi, B.J., Cambone, S.A., Veeraraghavan, A., Sabharwal, A., Jafari, R.,
A Survey on Smart Homes for Aging in Place: Toward Solutions to the Specific Needs of the Elderly,
SPMag(35), No. 5, September 2018, pp. 111-119.
IEEE DOI
Survey, Smart Homes. Wearable sensors, Monitoring, Smart homes, Aging, Intelligent sensors, Sensor systems
Edu, J.S.[Jide S.], Such, J.M.[Jose M.], Suarez-Tangil, G.[Guillermo],
Smart Home Personal Assistants: A Security and Privacy Review,
Surveys(53), No. 6, December 2020, pp. xx-yy.
DOI Link
Survey, Smart Home. Amazon Echo/Alexa, smart home, voice assistants, Smart home personal assistants, Google Home/assistant, Apple Home Pod/Siri
Stergiou, A.[Alexandros], Poppe, R.[Ronald],
Analyzing human-human interactions: A survey,
CVIU(188), 2019, pp. 102799.
Elsevier DOI
Survey, Interactions. Human-human interaction, Human interaction recognition, Human activity
Beyan, C.[Cigdem], Vinciarelli, A.[Alessandro], Bue, A.D.[Alessio Del],
Co-Located Human-Human Interaction Analysis Using Nonverbal Cues: A Survey,
Surveys(56), No. 5, November 2023, pp. xx-yy.
DOI Link
Survey, Human Interaction. nonverbal communication, human behavior understanding, social signals, Interaction analysis
Gudmundsson, J.[Joachim], Horton, M.[Michael],
Spatio-Temporal Analysis of Team Sports,
Surveys(50), No. 2, June 2017, pp. Article No 22.
DOI Link
Survey, Sports. State-of-the-art object tracking systems now produce spatio-temporal traces of player trajectories with high definition and high frequency, and this, in turn, has facilitated a variety of research efforts, across many disciplines, to extract insight from the trajectories. We survey recent research efforts that use spatio-temporal data from team sports as input and involve non-trivial computation. This article categorises the research efforts in a coherent framework and identifies a number of open research questions.
Wu, F.[Fei], Wang, Q.Z.[Qing-Zhong], Bian, J.[Jiang], Ding, N.[Ning], Lu, F.X.[Fei-Xiang], Cheng, J.[Jun], Dou, D.[Dejing], Xiong, H.[Haoyi],
A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications,
MultMed(25), 2023, pp. 7943-7966.
IEEE DOI
Survey, Sports.
Li, T., Chang, H., Wang, M., Ni, B., Hong, R., Yan, S.,
Crowded Scene Analysis: A Survey,
CirSysVideo(25), No. 3, March 2015, pp. 367-386.
IEEE DOI
Survey, Crowds. Analytical models
Draghici, A.[Adriana], van Steen, M.[Maarten],
A Survey of Techniques for Automatically Sensing the Behavior of a Crowd,
Surveys(51), No. 1, 2018, pp. Article No 21.
DOI Link
Survey, Crowds.
Ullah, F.U.M.[Fath U Min], Obaidat, M.S.[Mohammad S.], Ullah, A.[Amin], Muhammad, K.[Khan], Hijji, M.[Mohammad], Baik, S.W.[Sung Wook],
A Comprehensive Review on Vision-Based Violence Detection in Surveillance Videos,
Surveys(55), No. 10, February 2023, pp. xx-yy.
DOI Link
Survey, Violence Detection. video data, neural networks, deep learning, Artificial Intelligence, smart surveillance, big data
Lejmi, W.[Wafa], Ben Khalifa, A.[Anouar], Mahjoub, M.A.[Mohamed Ali],
Challenges and Methods of Violence Detection in Surveillance Video: A Survey,
CAIP19(II:62-73).
Springer DOI
Survey, Violence Detection.
d'Orazio, T., Leo, M.,
A review of vision-based systems for soccer video analysis,
PR(43), No. 8, August 2010, pp. 2911-2926.
Elsevier DOI
Survey, Activity Recognition. Soccer video analysis; Low-level and high-level feature extraction; Modeling of feature dynamics
Manafifard, M., Ebadi, H., Abrishami-Moghaddam, H.,
A survey on player tracking in soccer videos,
CVIU(159), No. 1, 2017, pp. 19-46.
Elsevier DOI
Survey, Player Tracking. Soccer
Remagnino, P.[Paolo], Jones, G.A.[Graeme A.], Paragios, N.[Nikos], Regazzoni, C.S.[Carlo S.],
Video-Based Surveillance Systems: Computer Vision and Distributed Processing,
KluwerBoston, November 2001. ISBN 0-7923-7632-3.
WWW Link. Survey, Surveillance. Survey of surveillance work. Industrial applications. Detection and Tracking. Event Detection and Analysis. Distributed Architectures. Buy this book: Video-Based Surveillance Systems: Computer Vision and Distributed Processing
Quintana, M., Menendez, J.M., Alvarez, F., Lopez, J.P.,
Improving retail efficiency through sensing technologies: A survey,
PRL(81), No. 1, 2016, pp. 3-10.
Elsevier DOI
Survey, Retail. Intelligent retail
Wang, B.[Bang],
Coverage problems in sensor networks: A survey,
Surveys(43), No. 4, October 2011, pp. xx-yy.
DOI Link
Survey, Sensor Networks. Sensor networks, which consist of sensor nodes each capable of sensing environment and transmitting data, have lots of applications in battlefield surveillance, environmental monitoring, industrial diagnostics, etc.
Mavrinac, A.[Aaron], Chen, X.[Xiang],
Modeling Coverage in Camera Networks: A Survey,
IJCV(101), No. 1, January 2013, pp. 205-226.
WWW Link.
Survey, Camera Networks. survey of geometric and topological coverage models
See also automatic calibration method for stereo-based 3D distributed smart camera networks, An.
de Farias, C.M.[Claudio M.], Li, W.[Wei], Delicato, F.C.[Flávia C.], Pirmez, L.[Luci], Zomaya, A.Y.[Albert Y.], Pires, P.F.[Paulo F.], de Souza, J.N.[José N.],
A Systematic Review of Shared Sensor Networks,
Surveys(48), No. 4, May 2016, pp. Article No 51.
DOI Link
Survey, Sensor Networks. A systematic literature survey on SSNs. The main goal of the article is to provide the reader with the opportunity to understand what has been done and what remains as open issues in this field, as well as which are the pivotal factors of this evolutionary design and how this kind of design can be exploited by a wide range of WSN applications.
Agarwal, P.K.[Pankaj K.], Guibas, L.J.[Leonidas J.], Edelsbrunner, H.[Herbert], Erickson, J.[Jeff], Isard, M.[Michael], Har-Peled, S.[Sariel], Hershberger, J.[John], Jensen, C.[Christian], Kavraki, L.[Lydia], Koehl, P.[Patrice], Lin, M.[Ming], Manocha, D.[Dinesh], Metaxas, D.N.[Dimitris N.], Mirtich, B.[Brian], Mount, D.M.[David M.], Muthukrishnan, S., Pai, D.[Dinesh], Sacks, E.[Elisha], Snoeyink, J.[Jack], Suri, S.[Subhash], Wolefson, O.[Ouri],
Algorithmic issues in modeling motion,
Surveys(34), No. 4, December 2002, pp. 550-527.
WWW Link. Survey, Motion.
Ullman, S.,
Analysis of Visual Motion by Biological and Computer Systems,
Computer(14), No. 8, August 1981, pp. 57-69.
And: RCV87(132-144). Survey, Motion. Motion, Survey. How do people (animals) analyze motion and how can computers do the same thing. Presents the basics of his work and other MIT work - Gaussian filter, zero crossings, matching, descriptions.
Monnier, Q.[Quentin], Pouli, T.[Tania], Kpalma, K.[Kidiyo],
Survey on fast dense video segmentation techniques,
CVIU(241), 2024, pp. 103959.
Elsevier DOI
Survey, Video Segmentation. Video semantic segmentation, Matting, Deep learning
Cuevas, C.[Carlos], Martínez, R.[Raquel], García, N.[Narciso],
Detection of stationary foreground objects: A survey,
CVIU(152), No. 1, 2016, pp. 41-57.
Elsevier DOI
Survey, Foreground Objects. Stationary foreground
Bouwmans, T.[Thierry],
Subspace Learning for Background Modeling: A Survey,
RPCS(2), No. 3, November 2009, pp. 223-234.
WWW Link.
Survey, Motion Detection.
Elhabian, S., El-Sayed, K., Ahmed, S.,
Moving Object Detection in Spatial Domain using Background Removal Techniques: State-of-Art,
RPCS(1), No. 1, January 2008, pp. 32-54.
PDF File.
WWW Link. Survey, Background Subtraction.
Bouwmans, T., El Baf, F., Vachon, B.,
Background Modeling using Mixture of Gaussians for Foreground Detection: A Survey,
RPCS(1), No. 3, November 2008, pp. 219-237.
PDF File. Survey, Background Subtraction.
And:
Statistical Background Modeling for Foreground Detection: A Survey,
HPRCV09(IV: 181-199). Survey, Background Subtraction.
Sobral, A.[Andrews], Vacavant, A.[Antoine],
A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos,
CVIU(122), No. 1, 2014, pp. 4-21.
Elsevier DOI
Survey, Background Subtraction.
Setitra, I.[Insaf], Larabi, S.[Slimane],
Background Subtraction Algorithms with Post-processing: A Review,
ICPR14(2436-2441)
IEEE DOI
Survey, Background Subtraction. Computational modeling
Szeliski, R.S.[Richard S.],
Image Alignment and Stitching,
World ScientificSingapore, 2007. ISBN: 978-1-933019-04-8 Survey, Image Stitching. Buy this book: IMAGE ALIGNMENT AND STITCHING (Foundations and Trends(R) in Computer Graphics and Vision(R))
Szeliski, R.S.[Richard S.],
Image Alignment and Stitching: A Tutorial,
FTCGV(2), Issue 1, 2006, pp. 1-104.
DOI Link
Survey, Mosaic. Published January 2007.
Ghosh, D.[Debabrata], Kaabouch, N.[Naima],
A survey on image mosaicing techniques,
JVCIR(34), No. 1, 2016, pp. 1-11.
Elsevier DOI
Survey, Image Mosaic. Image mosaicing
Sommer, L.W., Teutsch, M., Schuchert, T., Beyerer, J.,
A Survey on Moving Object Detection for Wide Area Motion Imagery,
WACV16(1-9)
IEEE DOI
Survey, Motion Detection. Cameras
Benezeth, Y., Jodoin, P.M., Emile, B., Laurent, H., Rosenberger, C.,
Review and evaluation of commonly-implemented background subtraction algorithms,
ICPR08(1-4).
IEEE DOI
Survey, Background Subtraction.
Battiato, S.[Sebastiano], Guarnera, M.I.[Mirko Ignazio], Messina, G.[Giuseppe], Tomaselli, V.[Valeria],
Recent Patents on Color Demosaicing,
RPCS(1), No. 3, November 2008, pp. 194-207.
WWW Link. Survey, Demosaicing.
Section, Multiple Entries: 19.4.3.16 Super Resolution, Survey, Evaluation, Overviews Chapter Contents (Back)
Survey, Super Resolution. Super Resolution.
van Ouwerkerk, J.D.,
Image super-resolution survey,
IVC(24), No. 10, 1 October 2006, pp. 1039-1052.
Elsevier DOI
Survey, Super Resolution. Single frame
Katsaggelos, A.K.[Aggelos K.], Mateos, J.[Javier], Molina, R.[Rafael],
Super Resolution of Images and Video,
Morgan Claypool2007. Synthesis Lectures on Image, Video, and Multimedia Processing Survey, Super Resolution.
WWW Link.
Bannore, V.[Vivek],
Iterative-Interpolation Super-Resolution Image Reconstruction: A Computationally Efficient Techniqu,
Springer2009, ISBN: 978-3-642-00384-4
WWW Link. Survey, Super Resolution. Buy this book: Iterative-Interpolation Super-Resolution Image Reconstruction: A Computationally Efficient Technique (Studies in Computational Intelligence)
Milanfar, P.[Peyman], (Ed.)
Super-Resolution Imaging,
CRC PressBoca Raton, FL, September 28, 2010. ISBN: 9781439819302
WWW Link. Buy this book: Super-Resolution Imaging (Digital Imaging and Computer Vision)
Code, Super-Resolution.
Survey, Super-Resolution.
Tian, J.[Jing], Ma, K.K.[Kai-Kuang],
A survey on super-resolution imaging,
SIViP(5), No. 3, September 2011, pp. 329-342.
WWW Link.
Survey, Super-Resolution.
Wang, Z.H.[Zhi-Hao], Chen, J.[Jian], Hoi, S.C.H.[Steven C. H.],
Deep Learning for Image Super-Resolution: A Survey,
PAMI(43), No. 10, October 2021, pp. 3365-3387.
IEEE DOI
Survey, Super Resolution. Deep learning, Degradation, Animals, Benchmark testing, Measurement, Image super-resolution, deep learning, Generative adversarial nets (GAN)
Liu, A.[Anran], Liu, Y.H.[Yi-Hao], Gu, J.J.[Jin-Jin], Qiao, Y.[Yu], Dong, C.[Chao],
Blind Image Super-Resolution: A Survey and Beyond,
PAMI(45), No. 5, May 2023, pp. 5461-5480.
IEEE DOI
Survey, Super-Resolution. Degradation, Mathematical models, Data models, Taxonomy, Superresolution, Adaptation models, Training, Deep learning, image super-resolution
Moser, B.B.[Brian B.], Raue, F.[Federico], Frolov, S.[Stanislav], Palacio, S.[Sebastian], Hees, J.[Jörn], Dengel, A.[Andreas],
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances,
PAMI(45), No. 8, August 2023, pp. 9862-9882.
IEEE DOI
Survey, Super-Resolution. Task analysis, Superresolution, Measurement, Indexes, PSNR, Computer architecture, Satellites, Artificial intelligence
Anwar, S.[Saeed], Khan, S.[Salman], Barnes, N.M.[Nick M.],
A Deep Journey into Super-Resolution: A Survey,
Surveys(53), No. 3, May 2020, pp. xx-yy.
DOI Link
Survey, Super-Resolution. deep learning, survey, Super-resolution (SR), generative adversarial networks (GANs), high-resolution (HR), convolutional neural networks (CNNs)
Jiang, J.J.[Jun-Jun], Wang, C.Y.[Chen-Yang], Liu, X.M.[Xian-Ming], Ma, J.Y.[Jia-Yi],
Deep Learning-Based Face Super-Resolution: A Survey,
Surveys(55), No. 1, January 2023, pp. xx-yy.
DOI Link
Survey, Face Super-Resolution. survey, Face super-resolution, deep learning, facial characteristics
Eichhardt, I.[Iván], Chetverikov, D.[Dmitry], Jankó, Z.[Zsolt],
Image-guided ToF depth upsampling: a survey,
MVA(28), No. 3-4, May 2017, pp. 267-282.
WWW Link.
Survey, Depth Super Resolution.
Zhong, Z.W.[Zhi-Wei], Liu, X.M.[Xian-Ming], Jiang, J.J.[Jun-Jun], Zhao, D.B.[De-Bin], Ji, X.Y.[Xiang-Yang],
Guided Depth Map Super-Resolution: A Survey,
Surveys(55), No. 14s, July 2023, pp. xx-yy.
DOI Link
Survey, Depth Super-Resolution. Survey, Super-Resolution. learning, prior, filtering, survey, Guided depth map super-resolution
Li, K.[Kai], Yang, S.H.[Sheng-Hao], Dong, R.T.[Run-Ting], Wang, X.Y.[Xiao-Ying], Huang, J.Q.A.[Jian-Qi-Ang],
Survey of single image super-resolution reconstruction,
IET-IPR(14), No. 11, September 2020, pp. 2273-2290.
DOI Link
Survey, Super-Resolution.
Nasrollahi, K.[Kamal], Moeslund, T.B.[Thomas B.],
Super-resolution: a comprehensive survey,
MVA(25), No. 6, 2014, pp. 1423-1468.
WWW Link.
Survey, Super-Resolution.
Lehmann, T.M., Gonner, C., Spitzer, K.,
Survey: interpolation methods in medical image processing,
MedImg(18), No. 11, November 1999, pp. 1049-1075.
IEEE Top Reference.
Survey, Interpolation.
See also Addendum: B-spline interpolation in medical image processing.
Koh, J.H.[Jai-Hyun], Lee, J.H.[Jang-Ho], Yoon, S.[Sungroh],
Single-image deblurring with neural networks: A comparative survey,
CVIU(203), 2021, pp. 103134.
Elsevier DOI
Survey, Deblurring. Deep learning, Image deblurring, Image restoration, Neural network
Kokaram, A.C.,
On Missing Data Treatment for Degraded Video and Film Archives: A Survey and a New Bayesian Approach,
IP(13), No. 3, March 2004, pp. 397-415.
IEEE DOI
Survey, Super Resolution.
Roberto e Souza, M.[Marcos], de Almeida Maia, H.[Helena], Pedrini, H.[Helio],
Survey on Digital Video Stabilization: Concepts, Methods, and Challenges,
Surveys(55), No. 3, March 2023, pp. xx-yy.
DOI Link
Survey, Stabilization. Video stabilization, motion estimation, motion smoothing
Tekalp, A.M.[A. Murat],
Deep Learning for Image/Video Restoration and Super-Resolution,
FTCGV(13), No. 1, 2022, pp. 1-110.
DOI Link
Survey, Video Super-Resolution.
Ngo, A.C.L.[Anh Cat Le], Phan, R.C.W.[Raphael C.W.],
Seeing the Invisible: Survey of Video Motion Magnification and Small Motion Analysis,
Surveys(52), No. 6, October 2019, pp. xx-yy.
DOI Link
Survey, Motion Magnification. Eulerian, motion representation, Motion magnification, Lagrangian, motion extraction, video motion, small motion
Ferreira, F.[Fausto], Veruggio, G.[Gianmarco], Caccia, M.[Massimo], Bruzzone, G.[Gabriele],
A survey on real-time motion estimation techniques for underwater robots,
RealTimeIP(11), No. 4, April 2016, pp. 693-711.
Springer DOI
Survey, Motion Estimation.
Saputra, M.R.U.[Muhamad Risqi U.], Markham, A.[Andrew], Trigoni, N.[Niki],
Visual SLAM and Structure from Motion in Dynamic Environments: A Survey,
Surveys(51), No. 2, June 2018, pp. Article No 37.
DOI Link
Survey, Shape from Motion.
Wang, G.H.[Guang-Hui], Wu, Q.M.J.[Q.M. Jonathan],
Guide to Three Dimensional Structure and Motion Factorization,
Springer2011, ISBN: 978-0-85729-045-8
WWW Link. Survey, Factorization. Survey, Structure from Motion. Buy this book: Guide to Three Dimensional Structure and Motion Factorization (Advances in Pattern Recognition)
Lu, Y.[Ye], Zhang, J.Z., Wu, Q.M.J., Li, Z.N.[Ze-Nian],
A survey of motion-parallax-based 3-D reconstruction algorithms,
SMC-C(34), No. 4, December 2004, pp. 532-548.
IEEE Abstract.
Survey, Motion Parallax.
Beauchemin, S.S.[Steven S.], Barron, J.L.[John L.],
The Computation of Optical-Flow,
Surveys(27), No. 3, September 1995, pp. 433-467.
DOI Link Survey, Optic Flow.
Mitiche, A., and Aggarwal, J.K.,
A Computational Analysis of Time-Varying Images,
HPRIP86(311-332). Survey, Motion. Motion, Survey.
Jacobson, L.[Lowell], Wechsler, H.[Harry],
Derivation of Optical Flow Using a Spatiotemporal-Frequency Approach,
CVGIP(38), No. 1, April 1987, pp. 29-65.
Elsevier DOI Survey, Motion. Motion, Survey. The approach includes Hildreth and Schunck. The paper has a nice survey of techniques and a lot of equations. There may be something here if you want optical flow.
Fortun, D.[Denis], Bouthemy, P.[Patrick], Kervrann, C.[Charles],
Optical flow modeling and computation: A survey,
CVIU(134), No. 1, 2015, pp. 1-21.
Elsevier DOI
Survey, Optical Flow. Optical flow
Zhai, M.L.[Ming-Liang], Xiang, X.Z.[Xue-Zhi], Lv, N.[Ning], Kong, X.D.[Xiang-Dong],
Optical flow and scene flow estimation: A survey,
PR(114), 2021, pp. 107861.
Elsevier DOI
Survey, Optical Flow. Motion analysis, Optical flow, Scene flow, Variational model, Deep learning, Convolutional neural networks (CNNs)
Schunck, B.G.[Brian],
Image Flow Segmentation and Estimation by Constraint Line Clustering,
PAMI(11), No. 10, October 1989, pp. 1010-1027.
IEEE DOI
Earlier:
Image Flow: Fundamentals and Algorithms,
MU88(23-80).
And:
Motion Segmentation and Estimation by Constraint Line Filtering,
CVWS84(58-62). Survey, Motion. Motion, Survey. Optical Flow, Evaluation. This papers discusses techniques for image flow analysis with discontinuities in the flow.
Barron, J.L., Fleet, D.J., and Beauchemin, S.S.,
Performance of Optical Flow Techniques,
IJCV(12), No. 1, February 1994, pp. 43-77.
Springer DOI
HTML Version.
WWW Link.
And: Add: Burkitt, T.A., CVPR92(236-242).
IEEE DOI Code, Optic Flow. Survey, Optic Flow. Survey of the field and a comparison of a variety of techniques. Compares quality of results, not execution time. Compares: Lucas/Kanade (
See also Generalized Image Matching by the Method of Differences. ), Fleet/Jepson (
See also Hierarchial Construction of Orientation and Velocity Selective Filters. ), Uras (
See also Computational Approach to Motion Perception, A. ), Nagel (
See also On a Constraint Equation for the Estimation of Displacement Rates in Image Sequences. ), Anandan (
See also Computational Framework and an Algorithm for the Measurement of Visual Motion, A. ), Horn/Shunck (
See also Determining Optical Flow. ), Singh (
See also Image-Flow Computation: An Estimation-Theoretic Framework and a Unified Perspective. ). Code for all of these is available from:
WWW Link.
Barron, J.L.[John L.],
A Survey of Approaches for Determining Optic Flow, Environmental Layout and Egomotion,
RBCV-TR-84-5, November 1984, Toronto. Survey, Optic Flow. A good survey of motion papers up to 1984, especially the optic flow papers. There are summaries of most of the equations that people use and a lot of diagrams.
Raudies, F.[Florian], Neumann, H.[Heiko],
A review and evaluation of methods estimating ego-motion,
CVIU(116), No. 5, May 2012, pp. 606-633.
Elsevier DOI
Survey, Ego-Motion.
And:
An Efficient Linear Method for the Estimation of Ego-Motion from Optical Flow,
DAGM09(11-20).
Springer DOI
Ego-motion estimation; Visual motion field; Robust estimators; Optic flow; Random sample consensus; m-Functions; Hough transform; Statistical bias; Consistency; Gaussian noise; Outlier noise
He, M.[Ming], Zhu, C.Z.[Chao-Zheng], Huang, Q.[Qian], Ren, B.[Baosen], Liu, J.T.[Jin-Tao],
A review of monocular visual odometry,
VC(36), No. 5, May 2020, pp. 1053-1065.
Springer DOI
Survey, Visual Odometry.
Barron, J.L.,
Computing Motion and Structure from Noisy, Time-Varying Image Velocity Information,
RBCV-TR-88-24, Toronto, August 1989, Ph.D.Thesis (CS). Survey, Motion. Motion, Survey. It appears that all you would want to know about structure given optical flow is given here.
Murray, N.[Niall], Ademoye, O.A.[Oluwakemi A.], Ghinea, G.[Gheorghita], Muntean, G.M.[Gabriel-Miro],
A Tutorial for Olfaction-Based Multisensorial Media Application Design and Evaluation,
Surveys(50), No. 5, November 2017, pp. Article No 67.
DOI Link
Survey, Smell. Smell. QoE evaluation.
Saon, G., Chien, J.T.,
Large-Vocabulary Continuous Speech Recognition Systems: A Look at Some Recent Advances,
SPMag(29), No. 3, 2012, pp. 18-33.
IEEE DOI
Survey, Speech Recognition.
Young, S., Gasic, M., Thomson, B., Williams, J.D.,
POMDP-Based Statistical Spoken Dialog Systems: A Review,
PIEEE(100), No. 5, May 2013, pp. 1160-1179.
IEEE DOI
Survey, Speech.
Huang, X.D.[Xue-Dong], Baker, J.[James], Reddy, R.[Raj],
A Historical Perspective of Speech Recognition,
CACM(57), No. 1, January 2014, pp. 94-103.
DOI Link
Survey, Speech Recognition. What do we know now that we did not know 40 years ago?
Kuo, S.M., Morgan, D.R.,
Active noise control: a tutorial review,
PIEEE(87), No. 6, June 1999, pp. 943-973.
IEEE DOI
Survey, Noise.
Li, H., Ma, B.,
TechWare: Speaker and Spoken Language Recognition Resources,
SPMag(27), No. 6, 2010, pp. 139-142.
IEEE DOI
Survey, Speech Recognition. Best of the Web
Hansen, J., Hasan, T.,
Speaker Recognition by Machines and Humans: A tutorial review,
SPMag(32), No. 6, November 2015, pp. 74-99.
IEEE DOI
Survey, Speaker Recognition. Audio systems
O'Shaughnessy, D.[Douglas],
Speech Enhancement: A Review of Modern Methods,
HMS(54), No. 1, February 2024, pp. 110-120.
IEEE DOI
Survey, Speech Enhancement. Acoustic distortion, Acoustics, Speech enhancement, Speech coding, Reverberation, Noise measurement, Microphones.
Bengio, Y.[Yoshua], Courville, A.[Aaron], Vincent, P.[Pascal],
Representation Learning: A Review and New Perspectives,
PAMI(35), No. 8, 2013, pp. 1798-1828.
IEEE DOI Survey, Learning.
Neural networks; Speech recognition; Boltzmann machine; Deep learning; representation learning; unsupervised learning
Section, Multiple Entries: 14.1.1 Pattern Recognition, General and Survey Articles Chapter Contents (Back)
Survey, Pattern Recognition. Survey, Clustering. Pattern Recognition.
Ho, Y.C., Agrawala, A.K.,
On Pattern Classification Algorithms: Introduction and Survey,
PIEEE(56), No. 12, December 1968, pp. 2102-2114. Survey, Pattern Recognition.
Kanal, L.N.,
Interactive Pattern Analysis and Classification Systems: A Survey and Commentary,
PIEEE(60), No. 10, October 1972, pp. 1200-1215. Survey, Pattern Recognition.
Verhagen, C.J.D.M.,
Some general remarks about pattern recognition; its definition; its relation with other disciplines; a literature survey,
PR(7), No. 3, September 1975, pp. 109-116.
Elsevier DOI
Survey, Pattern Recognition.
Kulkarni, S.R., Lugosi, G., Venkatesh, S.S.,
Learning-Pattern Classification: A Survey,
IT(44), No. 6, October 1998, pp. 2178-2206.
Survey, Pattern Recognition.
Bunke, H.[Horst], Kandel, A.[Abraham], Last, M.[Mark], (Eds.)
Applied Pattern Recognition,
Springer2008, ISBN: 978-3-540-76830-2
WWW Link. Survey, Pattern Recognition. Buy this book: Applied Pattern Recognition (Studies in Computational Intelligence)
Akay, B.[Bahriye], Karaboga, D.[Dervis],
A survey on the applications of artificial bee colony in signal, image, and video processing,
SIViP(9), No. 4, May 2015, pp. 967-990.
WWW Link.
Survey, Bee Colony.
Camastra, F.[Francesco],
Data dimensionality estimation methods: a survey,
PR(36), No. 12, December 2003, pp. 2945-2954.
Elsevier DOI
Survey, Dimensionality.
Ding, L.[Ling], Li, C.[Chao], Jin, D.[Di], Ding, S.[Shifei],
Survey of spectral clustering based on graph theory,
PR(151), 2024, pp. 110366.
Elsevier DOI
Survey, Spectral Clustering. Spectral clustering, Similarity graph, Graph cut, Laplacian matrix, Eigenvector
Toussaint, G.T.[Godfried T.],
Bibliography on Estimation of Misclassification,
IT(20), 1974, pp. 472-479. Survey, Evaluation. Complete listing of the research up to this time.
Fawcett, T.[Tom],
An introduction to ROC analysis,
PRL(27), No. 8, June 2006, pp. 861-874.
Elsevier DOI
Survey, ROC Analysis. Classifier evaluation; Evaluation metrics
Hao, X.J.[Xue-Jie], Liu, L.[Lu], Yang, R.J.[Rong-Jin], Yin, L.Y.[Lize-Yan], Zhang, L.[Le], Li, X.H.[Xiu-Hong],
A Review of Data Augmentation Methods of Remote Sensing Image Target Recognition,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link
Survey, Data Augmentation.
Zhou, K.Y.[Kai-Yang], Liu, Z.W.[Zi-Wei], Qiao, Y.[Yu], Xiang, T.[Tao], Loy, C.C.[Chen Change],
Domain Generalization: A Survey,
PAMI(45), No. 4, April 2023, pp. 4396-4415.
IEEE DOI
Survey, Domain Generalization. Data models, Speech recognition, Adaptation models, Face recognition, Soft sensors, Handwriting recognition, machine learning
Lu, J.W.[Ji-Wen], Hu, J.L.[Jun-Lin], Zhou, J.,
Deep Metric Learning for Visual Understanding: An Overview of Recent Advances,
SPMag(34), No. 6, November 2017, pp. 76-84.
IEEE DOI
Survey, Metric Learning. Euclidean distance, Extraterrestrial measurements, Face recognition, Image classification, Learning systems, Visualization
See also Deep Transfer Metric Learning.
Song, Y.S.[Yi-Sheng], Wang, T.[Ting], Cai, P.[Puyu], Mondal, S.K.[Subrota K.], Sahoo, J.P.[Jyoti Prakash],
A Comprehensive Survey of Few-Shot Learning: Evolution, Applications, Challenges, and Opportunities,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link
Survey, Few-Shot Learning. prior knowledge, meta-learning, low-shot learning, zero-shot learning, one-shot learning, Few-shot learning
Kaur, H.[Harsurinder], Pannu, H.S.[Husanbir Singh], Malhi, A.K.[Avleen Kaur],
A Systematic Review on Imbalanced Data Challenges in Machine Learning: Applications and Solutions,
Surveys(52), No. 4, September 2019, pp. Article No 79.
DOI Link
Survey, Imbalanced Data.
Zhang, Y.F.[Yi-Fan], Kang, B.Y.[Bing-Yi], Hooi, B.[Bryan], Yan, S.C.[Shui-Cheng], Feng, J.S.[Jia-Shi],
Deep Long-Tailed Learning: A Survey,
PAMI(45), No. 9, September 2023, pp. 10795-10816.
IEEE DOI
Survey, Long-Tailed.
Vandenhende, S.[Simon], Georgoulis, S.[Stamatios], van Gansbeke, W.[Wouter], Proesmans, M.[Marc], Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
Multi-Task Learning for Dense Prediction Tasks: A Survey,
PAMI(44), No. 7, July 2022, pp. 3614-3633.
IEEE DOI
Survey, Multi-Task Learning. Task analysis, Deep learning, Optimization, Neural networks, Taxonomy, Multi-task learning, convolutional neural networks
Zhang, J.[Jing], Li, W.Q.[Wan-Qing], Ogunbona, P.[Philip], Xu, D.[Dong],
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective,
Surveys(51), No. 1, February 2019, pp. Article No 7.
DOI Link
Survey, Transfer Learning.
Zhuang, F.Z.[Fu-Zhen], Qi, Z.Y.[Zhi-Yuan], Duan, K.Y.[Ke-Yu], Xi, D.B.[Dong-Bo], Zhu, Y.C.[Yong-Chun], Zhu, H.S.[Heng-Shu], Xiong, H.[Hui], He, Q.[Qing],
A Comprehensive Survey on Transfer Learning,
PIEEE(109), No. 1, January 2021, pp. 43-76.
IEEE DOI
Survey, Transfer Learning. Task analysis, Semisupervised learning, Data models, Covariance matrices, Machine learning, Adaptation models, Kernel, transfer learning
Madadi, Y.[Yeganeh], Seydi, V.[Vahid], Nasrollahi, K.[Kamal], Hosseini, R.[Reshad], Moeslund, T.B.[Thomas B.],
Deep visual unsupervised domain adaptation for classification tasks: A survey,
IET-IPR(14), No. 14, December 2020, pp. 3283-3299.
DOI Link
Survey, Domain Adaption.
Xu, M.Q.[Meng-Qiu], Wu, M.[Ming], Chen, K.X.[Kai-Xin], Zhang, C.[Chuang], Guo, J.[Jun],
The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation Methods Based on Remote Sensing Data,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link
Survey, Domain Adaptation.
Pourpanah, F.[Farhad], Abdar, M.[Moloud], Luo, Y.X.[Yu-Xuan], Zhou, X.L.[Xin-Lei], Wang, R.[Ran], Lim, C.P.[Chee Peng], Wang, X.Z.[Xi-Zhao], Wu, Q.M.J.[Q. M. Jonathan],
A Review of Generalized Zero-Shot Learning Methods,
PAMI(45), No. 4, April 2023, pp. 4051-4070.
IEEE DOI
Survey, Zero-Shot Learning. Semantics, Visualization, Training, Deep learning, Feature extraction, Data models, Computational modeling, variational auto-encoders
Chen, J.[Jiaoyan], Geng, Y.X.[Yu-Xia], Chen, Z.[Zhuo], Pan, J.Z.[Jeff Z.], He, Y.[Yuan], Zhang, W.[Wen], Horrocks, I.[Ian], Chen, H.J.[Hua-Jun],
Zero-Shot and Few-Shot Learning With Knowledge Graphs: A Comprehensive Survey,
PIEEE(111), No. 6, June 2023, pp. 653-685.
IEEE DOI
Survey, Zero-Shot Learning. Neural networks, Learning systmes, Knowledge management, Knowledge graphs, Image classification, Visualization, zero-shot learning (ZSL)
Galar, M.[Mikel], Fernandez, A.[Alberto], Barrenechea, E.[Edurne], Bustince, H.[Humberto], Herrera, F.[Francisco],
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes,
PR(44), No. 8, August 2011, pp. 1761-1776.
Elsevier DOI
Survey, Ensemble Clustering. Multi-classification; Pairwise learning; One-vs-one; One-vs-all; Decomposition strategies; Ensembles
Kumar, K.N.[Kummari Naveen], Mohan, C.K.[Chalavadi Krishna], Cenkeramaddi, L.R.[Linga Reddy],
The Impact of Adversarial Attacks on Federated Learning: A Survey,
PAMI(46), No. 5, May 2024, pp. 2672-2691.
IEEE DOI
Survey, Federated Learning. Surveys, Data models, Security, Data privacy, Servers, Transfer learning, Training, Adversarial attacks, visibility
Guan, H.[Hao], Yap, P.T.[Pew-Thian], Bozoki, A.[Andrea], Liu, M.X.[Ming-Xia],
Federated learning for medical image analysis: A survey,
PR(151), 2024, pp. 110424.
Elsevier DOI
Survey, Federated Learning. Federated learning, Machine learning, Medical image analysis, Data privacy
Jain, A.K., Murty, M.N., and Flynn, P.J.,
Data clustering: A review,
Surveys(31), 1999, No. 3, pp. 264-323.
DOI Link Survey, Pattern Recognition.
Jain, A.K.[Anil K.],
Data clustering: 50 years beyond K-means,
PRL(31), No. 8, 1 June 2010, pp. 651-666.
Elsevier DOI
Survey, Clustering. Award, PRL Most Cited. 2019-2011 Award, PRL Most Cited. 2010-2012 Data clustering; User's dilemma; Historical developments; Perspectives on clustering; King-Sun Fu prize
Manolakis, D., Truslow, E., Pieper, M., Cooley, T., Brueggeman, M.,
Detection Algorithms in Hyperspectral Imaging Systems: An Overview of Practical Algorithms,
SPMag(31), No. 1, January 2014, pp. 24-33.
IEEE DOI
Survey, Object Detection. hyperspectral imaging
Nasrabadi, N.M.,
Hyperspectral Target Detection : An Overview of Current and Future Challenges,
SPMag(31), No. 1, January 2014, pp. 34-44.
IEEE DOI
Survey, Hyperspectral Targets. hyperspectral imaging
Paoletti, M.E., Haut, J.M., Plaza, J., Plaza, A.,
Deep learning classifiers for hyperspectral imaging: A review,
PandRS(158), 2019, pp. 279-317.
Elsevier DOI
Survey, Hyperspectral Imaging. Deep learning (DL), Hyperspectral imaging (HSI), Earth observation (EO), Classification
Dahiya, N.[Neelam], Singh, S.[Sartajvir], Gupta, S.[Sheifali],
A Review on Deep Learning Classifier for Hyperspectral Imaging,
IJIG(23), No. 4 2023, pp. 2350036.
DOI Link
Survey, Deep Learning.
Schwenker, F.[Friedhelm], Trentin, E.[Edmondo],
Pattern classification and clustering: A review of partially supervised learning approaches,
PRL(37), No. 1, 2014, pp. 4-14.
Elsevier DOI
Survey, Learning. Partially supervised learning
Chen, Y.B.[Yan-Bei], Mancini, M.[Massimiliano], Zhu, X.T.[Xia-Tian], Akata, Z.[Zeynep],
Semi-Supervised and Unsupervised Deep Visual Learning: A Survey,
PAMI(46), No. 3, March 2024, pp. 1327-1347.
IEEE DOI
Survey, Semi-Supervised Learning. Data models, Visualization, Training, Task analysis, Semisupervised learning, Deep learning, Unsupervised learning, visual representation learning
Berger, K.[Katja], Caicedo, J.P.R.[Juan Pablo Rivera], Martino, L.[Luca], Wocher, M.[Matthias], Hank, T.[Tobias], Verrelst, J.[Jochem],
A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link
Survey, Active Learning.
de Lange, M.[Matthias], Aljundi, R.[Rahaf], Masana, M.[Marc], Parisot, S.[Sarah], Jia, X.[Xu], Leonardis, A.[Aleš], Slabaugh, G.[Gregory], Tuytelaars, T.[Tinne],
A Continual Learning Survey: Defying Forgetting in Classification Tasks,
PAMI(44), No. 7, July 2022, pp. 3366-3385.
IEEE DOI
Survey, Continual Learning. Task analysis, Knowledge engineering, Neural networks, Training, Training data, Learning systems, Interference, Continual learning, neural networks
Zhu, Z.D.[Zhuang-Di], Lin, K.X.[Kai-Xiang], Jain, A.K.[Anil K.], Zhou, J.[Jiayu],
Transfer Learning in Deep Reinforcement Learning: A Survey,
PAMI(45), No. 11, November 2023, pp. 13344-13362.
IEEE DOI
Survey, Transfer Learning.
Filippone, M.[Maurizio], Camastra, F.[Francesco], Masulli, F.[Francesco], Rovetta, S.[Stefano],
A survey of kernel and spectral methods for clustering,
PR(41), No. 1, January 2008, pp. 176-190.
Elsevier DOI
Award, Pattern Recognition. Survey, Clustering. Partitional clustering; Mercer kernels; Kernel clustering; Kernel fuzzy clustering; Spectral clustering
Geng, C.X.[Chuan-Xing], Huang, S.J.[Sheng-Jun], Chen, S.C.[Song-Can],
Recent Advances in Open Set Recognition: A Survey,
PAMI(43), No. 10, October 2021, pp. 3614-3631.
IEEE DOI
Survey, Open Set Recognition. Training, Testing, Task analysis, Semantics, Face recognition, Data visualization, Open set recognition/classification, one-shot learning
Yang, M.S.[Miin-Shen],
A Survey of Fuzzy Clustering,
MathCompMod(18), 1993, pp. 1-16. Survey, Clustering.
Zhang, S.[Sheng], Sim, T.[Terence],
Discriminant Subspace Analysis: A Fukunaga-Koontz Approach,
PAMI(29), No. 10, October 2007, pp. 1732-1745.
IEEE DOI
Survey, Discrminiant Analysis.
Earlier:
When Fisher meets Fukunaga-Koontz: A New Look at Linear Discriminants,
CVPR06(I: 323-329).
IEEE DOI
See also Application of the Karhunen-Loeve Expansion to Feature Selection and Ordering. Analyze the techniques and show the relationships.
Section, Multiple Entries: 14.2.20.6 Support Vector Machines, SVM, Surveys, Reviews, General Chapter Contents (Back)
Support Vector Machines. SVM. Survey, SVM.
Cristianini, N.[Nello], Schölkopf, B.[Bernhard],
Support Vector Machines and Kernel Methods: The New Generation of Learning Machines,
AIMag(23), No. 3, Fall 2002, pp. 31-41. Survey, SVM. Survey and general discussion.
Cristianini, N.[Nello], Shawe-Taylor, J.[John],
An Introduction to Support Vector Machines,
Cambridge University Press2000. Survey, SVM.
WWW Link. ISBN: 0 521 78019 5 Buy this book: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Abe, S.[Shigeo],
Support Vector Machines for Pattern Classification,
Springer-Verlag2010. ISBN: 978-1-84996-097-7
WWW Link. Survey, Support Vector Machines. Buy this book: Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition)
Overview and analysis of SVM techniques. Design, training.
Mountrakis, G.[Giorgos], Im, J.[Jungho], Ogole, C.[Caesar],
Support vector machines in remote sensing: A review,
PandRS(66), No. 3, May 2011, pp. 247-259.
Elsevier DOI
Survey, Support Vector Machines. Support vector machines; Review; Remote sensing; SVM; SVMs
Miller, W.F., and Shaw, A.C.,
Linguistic Methods in Picture Processing: A Survey,
FJCC68(279-290). Survey, Grammars.
Astolfi, G.[Gilberto], Rezende, F.P.C.[Fabio Prestes Cesar], de Andrade Porto, J.V.[Joao Vitor], Matsubara, E.T.[Edson Takashi], Pistori, H.[Hemerson],
Syntactic Pattern Recognition in Computer Vision: A Systematic Review,
Surveys(54), No. 3, April 2021, pp. xx-yy.
DOI Link
Survey, Syntactic Techniques. image representation, syntactic methods, formal languages, pattern recognition
Fu, K.S., Booth, T.L.,
Grammatical Inference: Introduction and Survey,
SMC(5), 1975, pp. 95-111; pp. 409-423. Survey, Grammars.
Safavian, S.R.[S. Rasoul], and Landgrebe, D.A.[David A.],
A Survey of Decision Tree Classifier Methodology,
SMC(21), No. 3, May 1991, pp. 660-674.
PDF File. Survey, Decision Tree.
Rokach, L., Maimon, O.[Oded],
Top-down induction of decision trees classifiers: A survey,
SMC-C(35), No. 4, November 2005, pp. 476-487.
IEEE DOI
Survey, Decision Tree.
Belgiu, M.[Mariana], Dragut, L.[Lucian],
Random forest in remote sensing: A review of applications and future directions,
PandRS(114), No. 1, 2016, pp. 24-31.
Elsevier DOI
Survey, Random Forests. Random forest
Section, Multiple Entries: 14.5.2 Learning, General Surveys, Overviews Chapter Contents (Back)
Survey, Learning. Learning. Neural Networks. GAN. CNN.
Poggio, T.[Tomaso], Shelton, C.R.[Christian R.],
Machine Learning, Machine Vision, and the Brain,
AIMag(20), No. 3, Fall 1999, pp. 37-55. Regularization. Support Vector Machines. Survey, Learning. Survey of learning focused on a vision domain. Regularization, Support Vector Machines. Applied to face and pedestrian recognition.
Ioannidou, A.[Anastasia], Chatzilari, E.[Elisavet], Nikolopoulos, S.[Spiros], Kompatsiaris, I.[Ioannis],
Deep Learning Advances in Computer Vision with 3D Data: A Survey,
Surveys(50), No. 2, June 2017, pp. Article No 20.
DOI Link
Survey, Deep Learning. This article surveys methods applying deep learning on 3D data and provides a classification based on how they exploit them. From the results of the examined works, we conclude that systems employing 2D views of 3D data typically surpass voxel-based (3D) deep models, which however, can perform better with more layers and severe data augmentation. Therefore, larger-scale datasets and increased resolutions are required.
McCann, M.T., Jin, K.H., Unser, M.,
Convolutional Neural Networks for Inverse Problems in Imaging: A Review,
SPMag(34), No. 6, November 2017, pp. 85-95.
IEEE DOI
Survey, Convolutional Neural Networks. Computed tomography, Image reconstruction, Image resolution, Image segmentation, Inverse problems, Linear programming, Noise reduction
Arulkumaran, K., Deisenroth, M.P., Brundage, M., Bharath, A.A.,
Deep Reinforcement Learning: A Brief Survey,
SPMag(34), No. 6, November 2017, pp. 26-38.
IEEE DOI
Survey, Deep Learning. Artificial intelligence, Learning (artificial intelligence), Machine learning, Neural networks, Signal processing algorithms, Visualization
Serban, A.[Alex], Poll, E.[Erik], Visser, J.[Joost],
Adversarial Examples on Object Recognition: A Comprehensive Survey,
Surveys(53), No. 3, June 2020, pp. xx-yy.
DOI Link
Survey, Adversairal Networks. security, robustness, machine learning, Adversarial examples
Goodfellow, I.[Ian], Pouget-Abadie, J.[Jean], Mirza, M.[Mehdi], Xu, B.[Bing], Warde-Farley, D.[David], Ozair, S.[Sherjil], Courville, A.[Aaron], Bengio, Y.[Yoshua],
Generative Adversarial Networks,
CACM(63), No. 11, November 2020, pp. 139-144.
DOI Link
Survey, GAN.
Wang, Z.W.[Zheng-Wei], She, Q.[Qi], Ward, T.E.[Tomas E.],
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy,
Surveys(54), No. 2, February 2021, pp. xx-yy.
DOI Link
Survey, GAN. Generative adversarial networks, loss-variants, stabilizing training, architecture-variants
Das, R.[Rangan], Sen, S.[Sagnik], Maulik, U.[Ujjwal],
A Survey on Fuzzy Deep Neural Networks,
Surveys(53), No. 3, May 2020, pp. xx-yy.
DOI Link
Survey, Deep Networks. parallel models, integrated models, sequential models, ensemble models, fuzzy systems, Deep architecture
Samek, W., Montavon, G., Lapuschkin, S., Anders, C.J., Müller, K.R.,
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications,
PIEEE(109), No. 3, March 2021, pp. 247-278.
IEEE DOI
Survey, Deep Learning. Deep learning, Systematics, Neural networks, Artificial intelligence, Machine learning, Unsupervised learning, neural networks
Saxena, D.[Divya], Cao, J.N.[Jian-Nong],
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions,
Surveys(54), No. 3, May 2021, pp. xx-yy.
DOI Link
Survey, GAN. GANs variants, GANs Survey, Image generation, GANs challenges, GANs, mode collapse, deep Generative models
Jabbar, A.[Abdul], Li, X.[Xi], Omar, B.[Bourahla],
A Survey on Generative Adversarial Networks: Variants, Applications, and Training,
Surveys(54), No. 8, October 2021, pp. xx-yy.
DOI Link
Survey, GAN. architectural-variants, stabilize training, Generative Adversarial Networks (GANs), applications
Chen, L.Y.[Lei-Yu], Li, S.B.[Shao-Bo], Bai, Q.[Qiang], Yang, J.[Jing], Jiang, S.L.[San-Long], Miao, Y.M.[Yan-Ming],
Review of Image Classification Algorithms Based on Convolutional Neural Networks,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link
Survey, CNN.
Soviany, P.[Petru], Ionescu, R.T.[Radu Tudor], Rota, P.[Paolo], Sebe, N.[Nicu],
Curriculum Learning: A Survey,
IJCV(130), No. 6, June 2022, pp. 1526-1565.
Springer DOI
Survey, Curriculum Learning.
Bond-Taylor, S.[Sam], Leach, A.[Adam], Long, Y.[Yang], Willcocks, C.G.[Chris G.],
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models,
PAMI(44), No. 11, November 2022, pp. 7327-7347.
IEEE DOI
Survey, Generative Modeling. Data models, Training, Computational modeling, Analytical models, Generative adversarial networks, Predictive models, Neurons, normalizing flows
Section, Multiple Entries: 14.5.10.1 Neural Networks: General, Survey, Special Issues Chapter Contents (Back)
Survey, Neural Networks. Neural Networks.
Zhang, G.P.,
Neural networks for classification: a survey,
SMC-C(30), No. 4, November 2000, pp. 451-462.
IEEE Top Reference.
Survey, Neural Networks.
Egmont-Petersen, M., de Ridder, D., Handels, H.,
Image processing with neural networks: A Review,
PR(35), No. 10, October 2002, pp. 2279-2301.
Elsevier DOI
Survey, Neural Networks. 200 applications.
Selva, J.[Javier], Johansen, A.S.[Anders S.], Escalera, S.[Sergio], Nasrollahi, K.[Kamal], Moeslund, T.B.[Thomas B.], Clapés, A.[Albert],
Video Transformers: A Survey,
PAMI(45), No. 11, November 2023, pp. 12922-12943.
IEEE DOI
Survey, Video Transformers.
Han, K.[Kai], Wang, Y.H.[Yun-He], Chen, H.T.[Han-Ting], Chen, X.H.[Xing-Hao], Guo, J.Y.[Jian-Yuan], Liu, Z.H.[Zhen-Hua], Tang, Y.[Yehui], Xiao, A.[An], Xu, C.J.[Chun-Jing], Xu, Y.X.[Yi-Xing], Yang, Z.H.[Zhao-Hui], Zhang, Y.[Yiman], Tao, D.C.[Da-Cheng],
A Survey on Vision Transformer,
PAMI(45), No. 1, January 2023, pp. 87-110.
IEEE DOI
Survey, Vision Transformer. Transformers, Task analysis, Encoding, Computational modeling, Visualization, Object detection, high-level vision, video
Yao, T.[Ting], Li, Y.[Yehao], Pan, Y.W.[Ying-Wei], Wang, Y.[Yu], Zhang, X.P.[Xiao-Ping], Mei, T.[Tao],
Dual Vision Transformer,
PAMI(45), No. 9, September 2023, pp. 10870-10882.
IEEE DOI
Survey, Vision Transformer.
Sze, V., Chen, Y.H., Yang, T.J., Emer, J.S.,
Efficient Processing of Deep Neural Networks: A Tutorial and Survey,
PIEEE(105), No. 12, December 2017, pp. 2295-2329.
IEEE DOI
Survey, Deep Neural Networks. Artificial intelligence, Benchmark testing, Biological neural networks, spatial architectures
Ivanovs, M.[Maksims], Kadikis, R.[Roberts], Ozols, K.[Kaspars],
Perturbation-based methods for explaining deep neural networks: A survey,
PRL(150), 2021, pp. 228-234.
Elsevier DOI
Survey, Explainable Networks. Deep learning, Explainable artificial intelligence, Perturbation-based methods
Abadal, S.[Sergi], Jain, A.[Akshay], Guirado, R.[Robert], Lopez-Alonso, J.[Jorge], Alarcon, E.[Eduard],
Computing Graph Neural Networks: A Survey from Algorithms to Accelerators,
Surveys(54), No. 9, October 2021, pp. xx-yy.
DOI Link
Survey, Graph Neural Networks. Graph neural networks, GNN algorithms, graph embeddings, accelerators
Krizhevsky, A.[Alex], Sutskever, I.[Ilya], Hinton, G.E.[Geoffrey E.],
ImageNet Classification with Deep Convolutional Neural Networks,
CACM(60), No. 6, June 2017, pp. 84-90.
DOI Link
Survey, Convolutional Networks.
Bai, X.[Xiao], Wang, X.[Xiang], Liu, X.L.[Xiang-Long], Liu, Q.[Qiang], Song, J.K.[Jing-Kuan], Sebe, N.[Nicu], Kim, B.[Been],
Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments,
PR(120), 2021, pp. 108102.
Elsevier DOI
Survey, Explainable Learning. Explainable deep learning, Network compression and acceleration, Adversarial robustness, Stability in deep learning
Zhang, X.W.[Xing-Wei], Zheng, X.L.[Xiao-Long], Mao, W.J.[Wen-Ji],
Adversarial Perturbation Defense on Deep Neural Networks,
Surveys(54), No. 8, October 2021, pp. xx-yy.
DOI Link
Survey, Adversarial Defense. security, deep neural networks, origin, Adversarial perturbation defense
Zhang, C.H.[Chen-Han], Yu, S.[Shui], Tian, Z.Y.[Zhi-Yi], Yu, J.J.Q.[James J. Q.],
Generative Adversarial Networks: A Survey on Attack and Defense Perspective,
Surveys(56), No. 4, November 2023, pp. xx-yy.
DOI Link
Survey, GAN Attacks. security and privacy, GANs survey, deep learning, attack and defense, Generative adversarial networks
Liu, M.Y.[Ming-Yu], Huang, X.[Xun], Yu, J.H.[Jia-Hui], Wang, T.C.[Ting-Chun], Mallya, A.[Arun],
Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications,
PIEEE(109), No. 5, May 2021, pp. 839-862.
IEEE DOI
Survey, Image Synthesis. Generators, Training, Generative adversarial networks, Linear programming, neural rendering
Qian, Z.[Zhuang], Huang, K.[Kaizhu], Wang, Q.F.[Qiu-Feng], Zhang, X.Y.[Xu-Yao],
A survey of robust adversarial training in pattern recognition: Fundamental, theory, and methodologies,
PR(131), 2022, pp. 108889.
Elsevier DOI
Survey, GAN Training. Adversarial examples, Adversarial training, Robust learning
Hruschka, E., Campello, R.J.G.B., Freitas, A.A., Ponce Leon, F., de Carvalho, A.C.,
A Survey of Evolutionary Algorithms for Clustering,
SMC-C(39), No. 2, March 2009, pp. 133-155.
IEEE DOI
Survey, Evolutionary Clustering.
Section, Multiple Entries: 22.2.1 Face Analysis, Evaluations, Benchmarks, Databases of Images Chapter Contents (Back)
Face Recognition. Application, Faces. Survey, Face Recognition. Evaluation, Faces. Recognition Bias discussion:
See also Bias in Face Analysis, Evaluaions, Fairness.
See also Face Verification, Authentication, Evaluations, Verification Benchmarks.
See also Face Analysis, General Papers, Surveys.
See also Face Image Quality.
Phillips, P.J.[P. Jonathon], Scruggs, W.T.[W. Todd], O'Toole, A.J.[Alice J.], Flynn, P.J.[Patrick J.], Bowyer, K.W.[Kevin W.], Schott, C.L.[Cathy L.], Sharpe, M.[Matthew],
FRVT 2006 and ICE 2006 Large-Scale Experimental Results,
PAMI(32), No. 5, May 2010, pp. 831-846.
IEEE DOI
Survey, Face Recognition. Evaluation, Face Recognition. From 2 large 2006 challange tests. In 2006, the best algorithms were better than people on unfamiliar faces.
Guo, Y.L.[Yu-Lan], Wang, H.[Hanyun], Wang, L.G.[Long-Guang], Lei, Y.J.[Yig-Jie], Liu, L.[Li], Bennamoun, M.[Mohammed],
3D Face Recognition: Two Decades of Progress and Prospects,
Surveys(56), No. 3, October 2023, pp. xx-yy.
DOI Link
Survey, Facial Expressions. pose variation, deep learning, local feature, facial occlusion, facial expression, 3D face recognition
Fabbrizzi, S.[Simone], Papadopoulos, S.[Symeon], Ntoutsi, E.[Eirini], Kompatsiaris, I.[Ioannis],
A survey on bias in visual datasets,
CVIU(223), 2022, pp. 103552.
Elsevier DOI
Survey, Dataset Bias. Computer vision, Visual datasets, Bias, AI ethics
Chellappa, R., Wilson, C.L., Sirohey, S.A.,
Human and Machine Recognition of Faces: A Survey,
PIEEE(83), No. 5, May 1995, pp. 705-740.
And: UMD-CAR-TR-731, August 1994.
And: Or TR-3339.
WWW Link. Survey, Face Recognition.
Samal, A.[Ashok], Iyengar, P.A.[Prasana A.],
Automatic Recognition and Analysis of Human Faces and Facial Expressions: A Survey,
PR(25), No. 1, January 1992, pp. 65-77.
Elsevier DOI Survey, Face Recognition. Survey, Facial Expressions.
Esch, J.,
Human and Machine Recognition of Faces: A Survey,
PIEEE(83), No. 5, May 1995, pp. 704-704. Survey, Face Recognition.
Valentin, D.[Dominique], Abdi, H.[Hervé], O'Toole, A.J.[Alice J.], Cottrell, G.W.[Garrison W.],
Connectionist Models of Face Processing: A Survey,
PR(27), No. 9, September 1994, pp. 1209-1230.
Elsevier DOI Survey, Face Recognition.
Essa, I.A.[Irfan A.],
Computers Seeing People,
AIMag(20), No. 2, Summer 1999, pp. 69-82. Survey, Face Recognition. General survey article with discussion of various ways programs are looking at people -- faces, gestures, tracking, etc.
Delac, K.[Kresimir], Grgic, M.[Mislav], Bartlett, M.S.[Marian Stewart],
Recent Advances in Face Recognition,
IN-TECHDecember 2008, Vienna, Austria. ISBN 978-953-7619-34-3.
WWW Link. The entire book is available for download with the above link. Survey, Face Recogniton.
Bowyer, K.W.[Kevin W.], Chang, K.I.[Kyong I.], Flynn, P.J.[Patrick J.],
A survey of approaches and challenges in 3D and multi-modal 3D-2D face recognition,
CVIU(101), No. 1, January 2005, pp. 1-15.
Elsevier DOI
Survey, Face Recognition. Award, CVIU, Most Cited. Honorable mention, 2005-2007. 38 cites.
Earlier:
A survey of approaches to three-dimensional face recognition,
ICPR04(I: 358-361).
IEEE DOI
See also Evaluation of Multimodal 2D+3D Face Biometrics, An.
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.,
Face Recognition: A Literature Survey,
Surveys(35), No. 4, December 2003, pp. 399-458.
DOI Link
Earlier: A1, A2, A4, A3: UMD--TR4167, October 2000.
WWW Link.
And: Revision: UMD-- TR4167R, August 2002.
PS File. Or:
PS File. Survey, Face Recognition.
Sinha, P., Balas, B., Ostrovsky, Y., Russell, R.,
Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About,
PIEEE(94), No. 11, November 2006, pp. 1948-1962.
IEEE DOI
Survey, Face Recognition.
Wechsler, H.[Harry],
Reliable Face Recognition Methods: System Design, Implementation and Evaluation,
Springer2007, ISBN 978-0-387-22372-8
WWW Link. Survey, Face Recognition. Survey of various techniques: The Human Face. Modeling and Prediction. Data Collection. Face Representation. Face Recognition. Face in A Crowd. 3D. Data Fusion. Denial and Deception. Augmented Cognition. Performance Evaluation. Error Analysis. Security and Privacy. e-Science and Computing. Buy this book: Reliable Face Recognition Methods: System Design, Implementation and Evaluation (International Series on Biometrics)
Zhang, X.Z.[Xiao-Zheng], Gao, Y.S.[Yong-Sheng],
Face recognition across pose: A review,
PR(42), No. 11, November 2009, pp. 2876-2896.
Elsevier DOI
Survey, Face Recognition. Face recognition; Pose variation; Survey; Review
Tistarelli, M.[Massimo], Bicego, M.[Manuele], Grosso, E.[Enrico],
Dynamic face recognition: From human to machine vision,
IVC(27), No. 3, February 2009, pp. 222-232.
Elsevier DOI
Survey, Face Recognition.
Earlier:
Recognizing People's Faces: from Human to Machine Vision,
ICARCV06(1-7).
IEEE DOI
Biometrics; Face recognition; Human perception
Chellappa, R.[Rama], Sinha, P.[Pawan], Phillips, P.J.[P. Jonathon],
Face Recognition by Computers and Humans,
Computer(43), No. 2, February 2010, pp. 46-55.
IEEE DOI
Survey, Face Recognition.
Phillips, P.J.[P. Jonathon],
Improving Face Recognition Technology,
Computer(44), No. 3, March 2011, pp. 84-86.
IEEE DOI
Survey, Face Recognition. Describes the great improvemnt in results for the various face recognition tests over a number of years.
Zhou, H.[Hailing], Mian, A., Wei, L.[Lei], Creighton, D., Hossny, M., Nahavandi, S.,
Recent Advances on Singlemodal and Multimodal Face Recognition: A Survey,
HMS(44), No. 6, December 2014, pp. 701-716.
IEEE DOI
Survey, Face Recognition. face recognition
Alsalibi, B.[Bisan], Venkat, I.[Ibrahim], Subramanian, K.G., Lutfi, S.L.[Syaheerah Lebai], de Wilde, P.[Philippe],
The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition,
Surveys(48), No. 1, August 2015, pp. 5:1-5:33.
DOI Link
Survey, Face Recongition. Face recognition
Guo, G.D.[Guo-Dong], Zhang, N.[Na],
A survey on deep learning based face recognition,
CVIU(189), 2019, pp. 102805.
Elsevier DOI
Survey, Face Recognition. Deep learning, Face recognition, Artificial Neural Network, Convolutional Neural Networks, Autoencoder, Generative Adversarial Networks
Tan, X.Y.[Xiao-Yang], Chen, S.C.[Song-Can], Zhou, Z.H.[Zhi-Hua], Zhang, F.Y.[Fu-Yan],
Face Recognition from a Single Image Per Person: A Survey,
PR(39), No. 9, September 2006, pp. 1725-1745.
Elsevier DOI
Survey, Face Recognition. Award, Pattern Recognition, Honorable Mention. Single training image per person
Dagnes, N.[Nicole], Vezzetti, E.[Enrico], Marcolin, F.[Federica], Tornincasa, S.[Stefano],
Occlusion detection and restoration techniques for 3D face recognition: a literature review,
MVA(29), No. 5, July 2018, pp. 789-813.
Springer DOI
Survey, Face Recognition.
Jin, X.[Xin], Tan, X.Y.[Xiao-Yang],
Face alignment in-the-wild: A Survey,
CVIU(162), No. 1, 2017, pp. 1-22.
Elsevier DOI
Survey, Face Alignment. Face, alignment
Zafeiriou, S.P.[Stefanos P.], Zhang, C.[Cha], Zhang, Z.Y.[Zheng-You],
A survey on face detection in the wild: Past, present and future,
CVIU(138), No. 1, 2015, pp. 1-24.
Elsevier DOI
Survey, Face Detection. Face detection
Liu, H.[Heng], Zheng, X.Y.[Xiao-Yu], Han, J.G.[Jun-Gong], Chu, Y.Z.[Yue-Zhong], Tao, T.[Tao],
Survey on GAN-based face hallucination with its model development,
IET-IPR(13), No. 14, 12 December 2019, pp. 2662-2672.
DOI Link
Survey, Face Hallucination.
Bhanu, B.[Bir], Han, J.[Ju],
Human Recognition at a Distance in Video,
Springer2010, ISBN: 978-0-85729-123-3
 
|
WWW Link. Survey, Human Recognition.
The issues for non-cooperating recognition.
Wang, Z.F.[Zhi-Fei], Miao, Z.J.[Zhen-Jiang], Wu, Q.M.J.[Q. M. Jonathan], Wan, Y.L.[Yan-Li], Tang, Z.[Zhen],
Low-resolution face recognition: a review,
VC(30), No. 4, April 2014, pp. 359-386.
WWW Link.
Survey, Face Recognition.
Patel, V.M.[Vishal M.], Chen, Y.C.[Yi-Chen], Chellappa, R.[Rama], Phillips, P.J.[P. Jonathon],
Dictionaries for image and video-based face recognition,
JOSA-A(31), No. 5, May 2014, pp. 1090-1103.
DOI Link
Survey, Face Recognition. Image processing; Pattern recognition; Machine vision; Algorithms
Fu, Y.[Yun], Guo, G.D.[Guo-Dong], Huang, T.S.[Thomas S.],
Age Synthesis and Estimation via Faces: A Survey,
PAMI(32), No. 11, November 2010, pp. 1955-1976.
IEEE DOI
Survey, Age Estimation.
Georgopoulos, M.[Markos], Panagakis, Y.[Yannis], Pantic, M.[Maja],
Modeling of facial aging and kinship: A survey,
IVC(80), 2018, pp. 58-79.
Elsevier DOI
Survey, Kinship. Survey, Age. Age estimation, Age progression, Age-invariant face recognition, Cross-age face verification, Kinship verification
Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.,
Local Binary Patterns and Its Application to Facial Image Analysis: A Survey,
SMC-C(41), No. 6, November 2011, pp. 765-781.
IEEE DOI
Survey, LBP. Methodology and application to facial image analysis.
Wang, N.N.[Nan-Nan], Tao, D.C.[Da-Cheng], Gao, X.B.[Xin-Bo], Li, X.L.[Xue-Long], Li, J.[Jie],
A Comprehensive Survey to Face Hallucination,
IJCV(106), No. 1, January 2014, pp. 9-30.
Springer DOI
Survey, Face Recognition.
Manal, E.R.[El Rhazi], Arsalane, Z.[Zarghili], Aicha, M.[Majda],
Survey on the approaches based geometric information for 3D face landmarks detection,
IET-IPR(13), No. 8, 20 June 2019, pp. 1225-1231.
DOI Link
Survey, Facial Landmarks.
Wu, Y.[Yue], Ji, Q.A.[Qi-Ang],
Facial Landmark Detection: A Literature Survey,
IJCV(127), No. 2, February 2019, pp. 115-142.
Springer DOI
Survey, Facial Landmarks.
Soltanpour, S.[Sima], Boufama, B.[Boubakeur], Wu, Q.M.J.[Q.M. Jonathan],
A survey of local feature methods for 3D face recognition,
PR(72), No. 1, 2017, pp. 391-406.
Elsevier DOI
Survey, Facial Features. Face, recognition
Song, F.Y.[Feng-Yi], Tan, X.Y.[Xiao-Yang], Chen, S.C.[Song-Can], Zhou, Z.H.[Zhi-Hua],
A literature survey on robust and efficient eye localization in real-life scenarios,
PR(46), No. 12, 2013, pp. 3157-3173.
Elsevier DOI
Survey, Eye Detection. Eye localization
Yang, M.H.[Ming-Hsuan], Kriegman, D.J.[David J.], Ahuja, N.[Narendra],
Detecting Faces in Images: A Survey,
PAMI(24), No. 1, January 2002, pp. 34-58.
IEEE DOI
HTML Version.
Survey, Face Detection. Survey of different factors and scopes.
Hjelmås, E.[Erik], Low, B.K.[Boon Kee],
Face Detection: A Survey,
CVIU(83), No. 3, September 2001, pp. 236-274.
DOI Link
Evaluation, Face Detection. Survey, Face Detection. Classify algorithms as feature-based or image-based. No standardized tests so no comparative evaluation, unless data from the original authors.
Lewis, M.B.[Michael B.], Ellis, H.D.[Hadyn D.],
How we detect a face: A survey of psychological evidence,
IJIST(13), No. 1, 2003, pp. 3-7.
DOI Link
Survey, Face Detection.
Li, S.Z.[Stan Z.], Lu, J.W.[Ju-Wei],
Face Detection, Alignment, and Recognition,
ETCV04(Chapter 9). Survey, Face Detection.
Abaza, A.[Ayman], Ross, A.[Arun], Hebert, C.[Christina], Harrison, M.A.F.[Mary Ann F.], Nixon, M.S.[Mark S.],
A survey on ear biometrics,
Surveys(45), No. 2, February 2013, pp. Article No 22.
DOI Link
Survey, Ear Biometrics.
Ganapathi, I.I.[Iyyakutti Iyappan], Ali, S.S.[Syed Sadaf], Prakash, S.[Surya], Vu, N.S.[Ngoc-Son], Werghi, N.[Naoufel],
A Survey of 3D Ear Recognition Techniques,
Surveys(55), No. 10, February 2023, pp. xx-yy.
DOI Link
Survey, Ear Recognition. verification/identification, 2D/3D Ear, Biometrics, age invariant, inheritance, ICP, data quality, local/global features
Liu, C.J.[Cheng-Jun], Wechsler, H.,
Learning the Face Space: Representation and Recognition,
ICPR00(Vol I: 249-256).
IEEE DOI
Survey, Face Recognition.
Zou, X.[Xuan], Kittler, J.V.[Josef V.], Messer, K.[Kieron],
Illumination Invariant Face Recognition: A Survey,
BTAS07(1-8).
IEEE DOI
Survey, Face Recognition.
Kakumanu, P., Makrogiannis, S., Bourbakis, N.G.,
A survey of skin-color modeling and detection methods,
PR(40), No. 3, March 2007, pp. 1106-1122.
Elsevier DOI
Survey, Color Models. Award, Pattern Recognition, Honorable Mention. Skin-color modeling; Skin detection; Color spaces and color constancy
Ghiass, R.S.[Reza Shoja], Arandjelovic, O.D.[Ognjen D.], Bendada, A.[Abdelhakim], Maldague, X.[Xavier],
Infrared face recognition: A comprehensive review of methodologies and databases,
PR(47), No. 9, 2014, pp. 2807-2824.
Elsevier DOI
Survey, Face Recognition. Survey
Murphy-Chutorian, E.[Erik], Trivedi, M.M.[Mohan Manubhai],
Head Pose Estimation in Computer Vision: A Survey,
PAMI(31), No. 4, April 2009, pp. 607-626.
IEEE DOI
Survey, Head Pose. Advantages and disadvantages of 90 characteristic methods. Listing of datasets available at the time.
Abate, A.F.[Andrea F.], Bisogni, C.[Carmen], Castiglione, A.[Aniello], Nappi, M.[Michele],
Head pose estimation: An extensive survey on recent techniques and applications,
PR(127), 2022, pp. 108591.
Elsevier DOI
Survey, Head Pose. Biometrics, Head pose estimation, Face recognition, Frontalization
Huttunen, H.[Heikki], Chen, K.[Ke], Thakur, A.[Abhishek], Krohn-Grimberghe, A.[Artus], Gencoglu, O.[Oguzhan], Ni, X.Y.[Xing-Yang], Al-Musawi, M.[Mohammed], Xu, L.[Lei], van Veen, H.J.[Hendrik Jacob],
Computer Vision for Head Pose Estimation: Review of a Competition,
SCIA15(65-75).
Springer DOI
Survey, Head Pose.
Hammoud, R.I.[Riad I.], (Ed.)
Passive Eye Monitoring: Algorithms, Applications and Experiments,
Springer-VerlagNew York, 2008 ISBN: 978-3-540-75411-4
WWW Link. Survey, Eye Tracking. Buy this book: Passive Eye Monitoring: Algorithms, Applications and Experiments (Signals and Communication Technology)
Duchowski, A.T.,
Eye Tracking Methodology: Theory and Practice,
Springer2007. ISBN 978-1-84628-608-7. Second Edition.
WWW Link. Survey, Eye Tracking. A guide to the setup and operation of such systems. Buy this book: Eye Tracking Methodology: Theory and Practice
Obaidellah, U.[Unaizah], Haek, M.A.[Mohammed Al], Cheng, P.C.H.[Peter C.H.],
A Survey on the Usage of Eye-Tracking in Computer Programming,
Surveys(51), No. 1, 2018, pp. Article No 5.
DOI Link
Survey, Eye Tracking.
Zhou, Z.H.[Zi-Heng], Zhao, G.Y.[Guo-Ying], Hong, X.P.[Xiao-Peng], Pietikäinen, M.[Matti],
A review of recent advances in visual speech decoding,
IVC(32), No. 9, 2014, pp. 590-605.
Elsevier DOI
Survey, Visual Speech. Visual speech decoding
Hansen, D.W.[Dan Witzner], Ji, Q.[Qiang],
In the Eye of the Beholder: A Survey of Models for Eyes and Gaze,
PAMI(32), No. 3, March 2010, pp. 478-500.
IEEE DOI
Survey, Gaze Determination.
Ghosh, S.[Shreya], Dhall, A.[Abhinav], Hayat, M.[Munawar], Knibbe, J.[Jarrod], Ji, Q.[Qiang],
Automatic Gaze Analysis: A Survey of Deep Learning Based Approaches,
PAMI(46), No. 1, January 2024, pp. 61-84.
IEEE DOI
Survey, Gaze Analysis.
Gaze Interaction Bibliography,
2001.
WWW Link. Survey, Gaze Tracking. A listing of gaze based interaction papers. Much of it is for interactions by disabled.
Morimoto, C.H.[Carlos H.], Mimica, M.R.M.[Marcio R.M.],
Eye gaze tracking techniques for interactive applications,
CVIU(98), No. 1, April 2005, pp. 4-24.
Elsevier DOI
Survey, Gaze Tracking. Review and evaluation of some techniques.
Mao, R., Li, G., Hildre, H.P., Zhang, H.,
A Survey of Eye Tracking in Automobile and Aviation Studies: Implications for Eye-Tracking Studies in Marine Operations,
HMS(51), No. 2, April 2021, pp. 87-98.
IEEE DOI
Survey, Eye Tracking. Visualization, Gaze tracking, Automobiles, Vehicle dynamics, Vehicles, Terminology, Manuals, marine operation
Plopski, A.[Alexander], Hirzle, T.[Teresa], Norouzi, N.[Nahal], Qian, L.[Long], Bruder, G.[Gerd], Langlotz, T.[Tobias],
The Eye in Extended Reality: A Survey on Gaze Interaction and Eye Tracking in Head-Worn Extended Reality,
Surveys(55), No. 3, March 2023, pp. xx-yy.
DOI Link
Survey, Gaze. head-worn, collaboration, interaction, gaze, selection, literature review, virtual reality, head-mounted, Eye tracking, augmented reality
Bisogni, C.[Carmen], Nappi, M.[Michele], Tortora, G.[Genoveffa], del Bimbo, A.[Alberto],
Gaze analysis: A survey on its applications,
IVC(144), 2024, pp. 104961.
Elsevier DOI
Survey, Gaze. Gaze analysis gaze, Applications, Human gaze, Biometrics, HCI, VR, AR, Healthcare
Shivappa, S.T., Trivedi, M.M., Rao, B.D.,
Audiovisual Information Fusion in Human-Computer Interfaces and Intelligent Environments: A Survey,
PIEEE(98), No. 10, October 2010, pp. 1692-1715.
IEEE DOI
Survey, Audio-Visual Fusion.
Fernandez-Lopez, A.[Adriana], Sukno, F.M.[Federico M.],
Survey on automatic lip-reading in the era of deep learning,
IVC(78), 2018, pp. 53-72.
Elsevier DOI
Survey, Lip Reading. Automatic lip-reading, Audio-visual corpora, Visual speech decoding, Deep learning systems, Multi-view lip-reading
Rajan, S.[Saranya], Chenniappan, P.[Poongodi], Devaraj, S.[Somasundaram], Madian, N.[Nirmala],
Facial expression recognition techniques: a comprehensive survey,
IET-IPR(13), No. 7, 30 May 2019, pp. 1031-1040.
DOI Link
Survey, Facial Expressions.
Zhao, X.B.[Xi-Bin], Zhu, J.J.[Jun-Jie], Luo, B.J.[Bing-Jun], Gao, Y.[Yue],
Survey on Facial Expression Recognition: History, Applications, and Challenges,
MultMedMag(28), No. 4, October 2021, pp. 38-44.
IEEE DOI
Survey, Facial Expression. Feature extraction, Generative adversarial networks, Face recognition, Deep learning, Vehicles, History, Roads
Jampour, M.[Mahdi], Javidi, M.[Malihe],
Multiview Facial Expression Recognition, A Survey,
AffCom(13), No. 4, October 2022, pp. 2086-2105.
IEEE DOI
Survey, Facial Expressions. Face recognition, Head, Feature extraction, Skin, Magnetic heads, Protocols, Image recognition, DNN
Zhi, R.C.[Rui-Cong], Liu, M.Y.[Meng-Yi], Zhang, D.Z.[De-Zheng],
A comprehensive survey on automatic facial action unit analysis,
VC(36), No. 5, May 2020, pp. 1067-1093.
Springer DOI
Survey, Facial Action Unit.
Alexandre, G.R.[Gilderlane Ribeiro], Soares, J.M.[José Marques], Pereira-Thé, G.A.[George André],
Systematic review of 3D facial expression recognition methods,
PR(100), 2020, pp. 107108.
Elsevier DOI
Survey, Facial Expressions. 3D facial expression recognition, Systematic literature review, Preprocessing techniques, Classification setup
Hassan, T.[Teena], Seuß, D.[Dominik], Wollenberg, J.[Johannes], Weitz, K.[Katharina], Kunz, M.[Miriam], Lautenbacher, S.[Stefan], Garbas, J.U.[Jens-Uwe], Schmid, U.[Ute],
Automatic Detection of Pain from Facial Expressions: A Survey,
PAMI(43), No. 6, June 2021, pp. 1815-1831.
IEEE DOI
Survey, Pain. Pain, Feature extraction, Task analysis, Imaging, Encoding, Observers, Machine learning, Automatic pain detection, survey
Zhao, G.Y.[Guo-Ying], Li, X.B.[Xiao-Bai], Li, Y.[Yante], Pietikäinen, M.[Matti],
Facial Micro-Expressions: An Overview,
PIEEE(111), No. 10, October 2023, pp. 1215-1235.
IEEE DOI
Survey, Micro-Expressions.
Section, Multiple Entries: 22.3.6.3 Facial Expressions, Overviews, Surveys, Data Chapter Contents (Back)
Survey, Facial Expressions. Face Recognition. Application, Faces. Expressions.
Pantic, M.[Maja], Rothkrantz, L.J.M.[Leon J.M.],
Automatic Analysis of Facial Expressions: The State of the Art,
PAMI(22), No. 12, December 2000, pp. 1424-1445.
IEEE DOI
Survey, Facial Expressions. 100+ references. Discusses a large number of techniques with how they do it. Holistic approach, analytic approach, hybrid approach. Static and video approaches.
Fasel, B., Luettin, J.[Juergen],
Automatic Facial Expression Analysis: A Survey,
PR(36), No. 1, January 2003, pp. 259-275.
Elsevier DOI
Survey, Facial Expressions.
Zeng, Z.H.[Zhi-Hong], Pantic, M.[Maja], Roisman, G.I.[Glenn I.], Huang, T.S.[Thomas S.],
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions,
PAMI(31), No. 1, January 2009, pp. 39-58.
IEEE DOI
Survey, Emotion Recogniton.
Shan, C.F.[Cai-Feng], Gong, S.G.[Shao-Gang], McOwan, P.W.[Peter W.],
Facial Expression Recognition Based on Local Binary Patterns: A Comprehensive Study,
IVC(27), No. 6, 4 May 2009, pp. 803-816.
Elsevier DOI
Survey, Facial Expressions. Facial expression recognition; Local Binary Patterns; Support vector machine; Adaboost; Linear discriminant analysis; Linear programming
Sandbach, G.[Georgia], Zafeiriou, S.P.[Stefanos P.], Pantic, M.[Maja], Yin, L.J.[Li-Jun],
Static and dynamic 3D facial expression recognition: A comprehensive survey,
IVC(30), No. 10, October 2012, pp. 683-697.
Elsevier DOI
Survey, Facial Expressions. Facial behaviour analysis; Facial expression recognition; 3D facial surface; 3D facial surface sequences (4D faces)
Vinola, C., Vimaladevi, K.,
A Survey on Human Emotion Recognition Approaches, Databases and Applications,
ELCVIA(14), No. 2, 2015, pp. xx-yy.
DOI Link
Survey, Emotion Recognition.
Deshmukh, S., Patwardhan, M., Mahajan, A.,
Survey on real-time facial expression recognition techniques,
IET-Bio(5), No. 3, 2016, pp. 155-163.
DOI Link
Survey, Facial Expressions. face recognition
Corneanu, C.A.[Ciprian A.], Oliu Simón, M.[Marc], Cohn, J.F., Guerrero, S.E.,
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications,
PAMI(38), No. 8, August 2016, pp. 1548-1568.
IEEE DOI
Survey, Emotion Recogniton.
Zhang, L.G.[Li-Gang], Verma, B.[Brijesh], Tjondronegoro, D.[Dian], Chandran, V.[Vinod],
Facial Expression Analysis under Partial Occlusion: A Survey,
Surveys(51), No. 2, June 2018, pp. Article No 25.
DOI Link
Survey, Facial Expressions.
Dhelim, S.[Sahraoui], Chen, L.M.[Li-Ming], Das, S.K.[Sajal K.], Ning, H.S.[Huan-Sheng], Nugent, C.[Chris], Leavey, G.[Gerard], Pesch, D.[Dirk], Bantry-White, E.[Eleanor], Burns, D.[Devin],
Detecting Mental Distresses Using Social Behavior Analysis in the Context of COVID-19: A Survey,
Surveys(55), No. 14s, July 2023, pp. xx-yy.
DOI Link
Survey, Mental Health. mental disorder detection, COVID-19, mental health, Social media analysis
Alarcão, S.M., Fonseca, M.J.,
Emotions Recognition Using EEG Signals: A Survey,
AffCom(10), No. 3, July 2019, pp. 374-393.
IEEE DOI
Survey, Emotions, EEG. Electroencephalography, Electrodes, Electric potential, Emotion recognition, Feature extraction, Brain, recognition
Li, X.[Xiang], Zhang, Y.Z.[Ya-Zhou], Tiwari, P.[Prayag], Song, D.W.[Da-Wei], Hu, B.[Bin], Yang, M.H.[Mei-Hong], Zhao, Z.G.[Zhi-Gang], Kumar, N.[Neeraj], Marttinen, P.[Pekka],
EEG Based Emotion Recognition: A Tutorial and Review,
Surveys(55), No. 4, April 2023, pp. xx-yy.
DOI Link
Survey, EEG Emotion. psychophysiological computing, Affective Computing, emotion recognition, EEG
Section, Multiple Entries: 22.3.6.2.2 Emotion Recognition, Survey, General, Review, Datasets, Database Chapter Contents (Back)
Emotion Recognition. Survey, Emotion Recognition.
Sariyanidi, E.[Evangelos], Gunes, H.[Hatice], Cavallaro, A.[Andrea],
Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition,
PAMI(37), No. 6, June 2015, pp. 1113-1133.
IEEE DOI
Survey, Facial Affect.
Earlier:
Probabilistic Subpixel Temporal Registration for Facial Expression Analysis,
ACCV14(IV: 320-335).
Springer DOI
Emotion recognition
Wang, S., Ji, Q.,
Video Affective Content Analysis: A Survey of State-of-the-Art Methods,
AffCom(6), No. 4, October 2015, pp. 410-430.
IEEE DOI
Survey, Affect Analsis. Content analysis
Devi, B.[Bhagyashri], Preetha, M.M.S.J.[M. Mary Synthuja Jain],
A Descriptive Survey on Face Emotion Recognition Techniques,
IJIG(23), No. 1 2023, pp. 2350008.
DOI Link
Survey, Emotions.
Sarafianos, N.[Nikolaos], Boteanu, B.[Bogdan], Ionescu, B.[Bogdan], Kakadiaris, I.A.[Ioannis A.],
3D Human pose estimation: A review of the literature and analysis of covariates,
CVIU(152), No. 1, 2016, pp. 1-20.
Elsevier DOI
Survey, Human Pose. 3D Human pose estimation
Desmarais, Y.[Yann], Mottet, D.[Denis], Slangen, P.[Pierre], Montesinos, P.[Philippe],
A review of 3D human pose estimation algorithms for markerless motion capture,
CVIU(212), 2021, pp. 103275.
Elsevier DOI
Survey, Human Pose. 3D human pose estimation, Convolutional neural networks, Survey
Liu, W.[Wu], Bao, Q.[Qian], Sun, Y.[Yu], Mei, T.[Tao],
Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective,
Surveys(55), No. 4, April 2023, pp. xx-yy.
DOI Link
Survey, Human Pose. Human pose estimation, 2D and 3D pose, deep learning, monocular images
Werghi, N.[Naoufel],
Segmentation and Modeling of Full Human Body Shape From 3-D Scan Data: A Survey,
SMC-C(37), No. 6, November 2007, pp. 1122-1136.
IEEE DOI
Survey, Human Shape.
Wang, J.B.[Jin-Bao], Tan, S.J.[Shu-Jie], Zhen, X.T.[Xian-Tong], Xu, S.[Shuo], Zheng, F.[Feng], He, Z.Y.[Zhen-Yu], Shao, L.[Ling],
Deep 3D human pose estimation: A review,
CVIU(210), 2021, pp. 103225.
Elsevier DOI
Survey, Human Pose. 3D Human Pose Estimation, Deep Learning
Cheng, W.H.[Wen-Huang], Song, S.[Sijie], Chen, C.Y.[Chieh-Yun], Hidayati, S.C.[Shintami Chusnul], Liu, J.[Jiaying],
Fashion Meets Computer Vision: A Survey,
Surveys(54), No. 4, July 2021, pp. xx-yy.
DOI Link
Survey, Fashion. fashion detection, fashion analysis, Intelligent fashion, fashion synthesis, fashion recommendation
Deldjoo, Y.[Yashar], Nazary, F.[Fatemeh], Ramisa, A.[Arnau], McAuley, J.J.[Julian J.], Pellegrini, G.[Giovanni], Bellogin, A.[Alejandro], di Noia, T.[Tommaso],
A Review of Modern Fashion Recommender Systems,
Surveys(56), No. 4, October 2023, pp. xx-yy.
DOI Link
Survey, Fashion. fashion retail, Recommender systems, artificial intelligence, information retrieval, e-commerce
Ding, Y.J.[Yu-Juan], Lai, Z.H.[Zhi-Hui], Mok, P.Y., Chua, T.S.[Tat-Seng],
Computational Technologies for Fashion Recommendation: A Survey,
Surveys(56), No. 5, November 2023, pp. xx-yy.
DOI Link
Survey, Fashion. outfit recommendation, compatibility modeling, personalized recommendation, fashion survey, Fashion recommendation
Zhu, W.T.[Wen-Tao], Ma, X.X.[Xiao-Xuan], Ro, D.[Dongwoo], Ci, H.[Hai], Zhang, J.[Jinlu], Shi, J.X.[Jia-Xin], Gao, F.[Feng], Tian, Q.[Qi], Wang, Y.Z.[Yi-Zhou],
Human Motion Generation: A Survey,
PAMI(46), No. 4, April 2024, pp. 2430-2449.
IEEE DOI
Survey, Human Motion Generation. Surveys, Shape, Motion capture, Biological system modeling, Data collection, Human motion, generative model, deep learning, literature survey
Lan, G.[Gongjin], Wu, Y.[Yu], Hu, F.[Fei], Hao, Q.[Qi],
Vision-Based Human Pose Estimation via Deep Learning: A Survey,
HMS(53), No. 1, February 2023, pp. 253-268.
IEEE DOI
Survey, Human Pose. Heating systems, Task analysis, Measurement, Market research, Deep learning, Pose estimation, Pipelines, Action recognition, human pose estimation (HPE)
Zheng, C.[Ce], Wu, W.H.[Wen-Han], Chen, C.[Chen], Yang, T.J.N.[Tao-Jian-Nan], Zhu, S.J.[Si-Jie], Shen, J.[Ju], Kehtarnavaz, N.[Nasser], Shah, M.[Mubarak],
Deep Learning-Based Human Pose Estimation: A Survey,
Surveys(56), No. 1, August 2023, pp. 11.
DOI Link
Survey, Pose Estimation. deep learning-based pose estimation, pose estimation datasets, Survey of human pose estimation, pose estimation metrics, 2D and 3D pose estimation
Section, Multiple Entries: 22.4.2.1 Gesture, Overviews, Surveys, Evaluations Chapter Contents (Back)
Survey, Gesture. Hand Gestures. Gesture.
Segen, J.[Jakub], Kumar, S.[Senthil],
Look Ma, No Mouse!: Simplifying Human-Computer Interaction by Using Hand Gestures,
CACM(43), No. 7, July 2000, pp. 102-109. Survey, Gesture. More a survey, but summarizes a lot of work.
Turk, M.A.[Matthew A.], Kölsch, M.[Mathias],
Perceptual Interfaces,
ETCV04(Chapter 10). Survey, HCI.
Murtagh, F., Taskaya, T., Contreras, P., Mothe, J., Englmeier, K.,
Interactive Visual User Interfaces: A Survey,
AIR(19), No. 4, June 2003, pp. 263-283.
WWW Link.
Survey, GUI.
Mitra, S.[Sushmita], Acharya, T.[Tinku],
Gesture Recognition: A Survey,
SMC-C(37), No. 3, May 2007, pp. 311-324.
IEEE DOI
Survey, Gesture.
Erol, A.[Ali], Bebis, G.N.[George N.], Nicolescu, M.[Mircea], Boyle, R.D.[Richard D.], Twombly, X.[Xander],
Vision-based hand pose estimation: A review,
CVIU(108), No. 1-2, October-November 2007, pp. 52-73.
Elsevier DOI
Survey, Hand Pose.
Earlier:
A Review on Vision-Based Full DOF Hand Motion Estimation,
VHCI05(III: 75-75).
IEEE DOI
Hand pose estimation; Gesture recognition; Gesture-based HCI
Bolton, M.L., Bass, E.J., Siminiceanu, R.I.,
Using Formal Verification to Evaluate Human-Automation Interaction: A Review,
SMCS(43), No. 3, May 2013, pp. 488-503.
IEEE DOI
Survey, HCI.
Turk, M.[Matthew],
Multimodal interaction: A review,
PRL(36), No. 1, 2014, pp. 189-195.
Elsevier DOI
Survey, Interaction. Multimodal interaction
Pisharady, P.K.[Pramod Kumar], Saerbeck, M.[Martin],
Recent methods and databases in vision-based hand gesture recognition: A review,
CVIU(141), No. 1, 2015, pp. 152-165.
Elsevier DOI
Survey, Hand Gestures. Gesture recognition
Cheng, H.[Hong], Yang, L.[Lu], Liu, Z.C.[Zi-Cheng],
Survey on 3D Hand Gesture Recognition,
CirSysVideo(26), No. 9, September 2016, pp. 1659-1673.
IEEE DOI
Survey, Hand Gestures. Assistive technology
Li, R.[Rui], Liu, Z.Y.[Zhen-Yu], Tan, J.R.[Jian-Rong],
A survey on 3D hand pose estimation: Cameras, methods, and datasets,
PR(93), 2019, pp. 251-272.
Elsevier DOI
Survey, Hand Pose. Hand pose estimation, Hand tracking, Depth camera, Human-computer interaction
Corso, J.J.[Jason J.], Ye, G.Q.[Guang-Qi], Burschka, D.[Darius], Hager, G.D.[Gregory D.],
A Practical Paradigm and Platform for Video-Based Human-Computer Interaction,
Computer(41), No. 5, May 2008, pp. 48-55.
IEEE DOI
Survey, HCI.
Tzovaras, D.[Dimitros], (Ed.)
Multimodal User Interfaces: From Signals to Interaction,
Springer2008, ISBN: 978-3-540-78344-2.
WWW Link. Survey, HCI. Collection of papers on HCI systems. Buy this book: Multimodal User Interfaces: From Signals to Interaction (Signals and Communication Technology)
Doherty, K.[Kevin], Doherty, G.[Gavin],
Engagement in HCI: Conception, Theory and Measurement,
Surveys(51), No. 5, January 2019, pp. Article No 99.
DOI Link
Survey, HCI.
Gammulle, H.[Harshala], Ahmedt-Aristizabal, D.[David], Denman, S.[Simon], Tychsen-Smith, L.[Lachlan], Petersson, L.[Lars], Fookes, C.[Clinton],
Continuous Human Action Recognition for Human-Machine Interaction: A Review,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link
Survey, Human-Computer. neural networks, Datasets
Bellucci, A.[Andrea], Malizia, A.[Alessio], Aedo, I.[Ignacio],
Light on horizontal interactive surfaces: Input space for tabletop computing,
Surveys(46), No. 3, February 2014, pp. Article No 32.
DOI Link
Survey, Gesture Recognition. The rising demand for the digital support of human activities motivated the need to bring computational power to
Magrofuoco, N.[Nathan], Roselli, P.[Paolo], Vanderdonckt, J.[Jean],
Two-Dimensional Stroke Gesture Recognition: A Survey,
Surveys(54), No. 7, July 2021, pp. xx-yy.
DOI Link
Survey, Gestures. touch gestures, Gesture-based interfaces, gesture recognition, stroke gestures
Ahmed, S.[Shahzad], Kallu, K.D.[Karam Dad], Ahmed, S.[Sarfaraz], Cho, S.H.[Sung Ho],
Hand Gestures Recognition Using Radar Sensors for Human-Computer-Interaction: A Review,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link
Survey, Gestures.
Grabham, D.[Dan],
Beyond Kinect: Microsoft's vision for next-gen interfaces,
TechRadar(News), January 2011.
WWW Link. Survey, Gesture Recognition. Interview with Andrew Blake: How we'll interact with future tech General discussion. News item.
Ji, Y., Yang, Y., Shen, F., Shen, H.T., Li, X.,
A Survey of Human Action Analysis in HRI Applications,
CirSysVideo(30), No. 7, July 2020, pp. 2114-2128.
IEEE DOI
Survey, Human Robot Interaction. Human-robot interaction, action recognition, action prediction, action imitation, datasets, robots
Ong, S.C.W.[Sylvie C.W.], Ranganath, S.[Surendra],
Automatic sign language analysis: A survey and the future beyond lexical meaning,
PAMI(27), No. 6, June 2005, pp. 873-891.
IEEE Abstract.
Survey, Gesture.
Earlier:
Deciphering gestures with layered meanings and signer adaptation,
AFGR04(559-564).
IEEE DOI
Earlier:
Classification of Gesture with Layered Meanings,
GW03(239-246).
Springer DOI
Nonmanual signals and grammatical processes, which result in systematic variations in sign appearance, are integral to recognition.
Ratha, N.K.[Nalini K.], Govindaraju, V.[Venu], (Eds.)
Advances in Biometrics Sensors, Algorithms and Systems,
Springer2008, ISBN: 978-1-84628-920-0.
WWW Link. Survey, Biometrics. Collection of papers on biometrics. Buy this book: Advances in Biometrics: Sensors, Algorithms and Systems
Petrovska-Delacrétaz, D.[Dijana], Chollet, G.[Gérard], Dorizzi, B.[Bernadette],
Guide to Biometric Reference Systems and Performance Evaluation,
Springer2009, ISBN: 978-1-84800-291-3.
WWW Link. Survey, Biometrics. Collection of papers on biometrics, especially evaluations. Buy this book: Guide to Biometric Reference Systems and Performance Evaluation
Tistarelli, M.[Massimo], Li, S.Z.[Stan Z.], Chellappa, R.[Rama], (Eds.)
Handbook of Remote Biometrics for Surveillance and Security,
Springer2009, ISBN: 978-1-84882-384-6
WWW Link. Survey, Biometrics. Techniques to apply at a distance. Gait, multiple methods, security, privacy. Buy this book: Handbook of Remote Biometrics: for Surveillance and Security (Advances in Pattern Recognition)
Li, S.Z.[Stan Z.], Jain, A.K.[Anil K.], (Eds.)
Encyclopedia of Biometrics,
Springer2009, ISBN: 978-0-387-73002-8
WWW Link. Survey, Biometrics. 250 overview and 800 definition entries. Buy this book: Encyclopedia of Biometrics
Maiorana, E.[Emanuele],
A survey on biometric recognition using wearable devices,
PRL(156), 2022, pp. 29-37.
Elsevier DOI
Survey, Biometrics. Biometrics, Wearable devices
Section, Multiple Entries: 22.5.1 Biometrics, Surveys, Analysis, Datasets, Legal Issues, Philosophy Chapter Contents (Back)
Survey, Biometrics. Biometrics.
See also Biometrics, Privacy Issues, Security Issues. For some similar privacy issues:
See also Surveillance Systems, Privacy Protection, Issues, Techniques, Face Obscuration.
Lawton, G.[George],
Biometrics: A New Era in Security,
Computer(31), No. 8, August 1998, pp. 16-18. Survey, Biometrics. An Industry Trends column that provides a brief survey of the field. Not a technical paper.
Woodward, J.D.[John D.], Orlans, N.M.[Nicholas M.], Higgins, P.T.[Peter T.],
Biometrics,
McGraw-Hill2003. ISBN 0-07-222227-1 Survey, Biometrics. A general review of the techniques and issues of biometrics. Buy this book: Biometrics
Jain, A.K., Ross, A., Prabhakar, S.,
An Introduction to Biometric Recognition,
CirSysVideo(14), No. 1, January 2004, pp. 4-20.
IEEE Abstract.
Survey, Biometrics.
Hammoud, R.I., Abidi, B.R., Abidi, M.A., (Eds.)
Face Biometrics for Personal Identification: Multi-Sensory Multi-Modal Systems,
Springer2007, ISBN 978-1-84628-501-1.
WWW Link. Survey, Biometrics. Covers a range of biometric traits including facial geometry, 3D ear form, fingerprints, vein structure, voice, and gait, its main emphasis is placed on multi-sensory and multi-modal face biometrics algorithms and systems. Buy this book: Digital Document Processing: Major Directions and Recent Advances (Advances in Pattern Recognition)
Yanushkevich, S.N.[Svetlana N], Gavrilova, M.L.[Marina L.], Wang, P.S.P.[Patrick S.P.], Srihari, S.N.[Sargur N.],
Image Pattern Recognition: Synthesis and Analysis in biometrics,
World ScientificMay, 2007. ISBN: 978-981-256-908-0 Survey, Biometrics. Buy this book: Image Pattern Recognition: Synthesis and Analysis in Biometrics (Series in Machine Perception & Artifical Intelligence) (Series in Machine Perception & Artifical Intelligence)
Jain, A.K.[Anil K.], Flynn, P.J.[Patrick J.], Ross, A.A.[Arun A.],
Handbook of Biometrics,
Springer-VerlagNew York, 2008 ISBN: 978-0-387-71040-2
WWW Link. Survey, Biometrics. Buy this book: Handbook of Biometrics
Vetter, R.[Ron],
Authentication by Biometric Verification,
Computer(43), No. 2, February 2010, pp. 28-29.
IEEE DOI
Survey, Biometrics.
Ricanek, Jr., K.[Karl], Savvides, M.[Marios], Woodard, D.L.[Damon L.], Dozier, G.[Gerry],
Unconstrained Biometric Identification: Emerging Technologies,
Computer(43), No. 2, February 2010, pp. 56-62.
IEEE DOI
Survey, Biometrics.
Ricanek Jr., K.[Karl], Boehnen, C.[Chris],
Facial Analytics: From Big Data to Law Enforcement,
Computer(45), No. 9, September 2012, pp. 95-97.
IEEE DOI
Survey, Face Recognition. Review of facial recognition systems and their use.
Jain, A.K.[Anil K.], Ross, A.A.[Arun A.], Nandakumar, K.[Karthik],
Introduction to Biometrics,
SpringerNew-York, 2011. ISBN: 978-0-387-77325-4
WWW Link. Buy this book: Introduction to Biometrics
Survey, Biometrics.
Wang, P.S.P.[Patrick S.P.], (Ed.)
Pattern Recognition, Machine Intelligence and Biometrics,
SpringerNew-York, 2012. ISBN: 978-3-642-22406-5
WWW Link. Buy this book: Pattern Recognition, Machine Intelligence and Biometrics
Survey, Biometrics.
Islam, S.M.S.[Syed M.S.], Bennamoun, M.[Mohammed], Owens, R.A.[Robyn A.], Davies, R.[Rowan],
A review of recent advances in 3D ear- and expression-invariant face biometrics,
Surveys(44), No. 3, June 2012, pp. Article No 14.
DOI Link
Survey, Biometrics.
Unar, J.A., Seng, W.C.[Woo Chaw], Abbasi, A.[Almas],
A review of biometric technology along with trends and prospects,
PR(47), No. 8, 2014, pp. 2673-2688.
Elsevier DOI
Survey, Biometrics. Award, Pattern Recognition, Honorable Mention. Biometrics
Bharadwaj, S.[Samarth], Vatsa, M.[Mayank], Singh, R.[Richa],
Biometric quality: a review of fingerprint, iris, and face,
JIVP(2014), No. 1, 2014, pp. 34.
DOI Link
Survey, Biometrics.
Earlier:
Can holistic representations be used for face biometric quality assessment?,
ICIP13(2792-2796)
IEEE DOI
biometrics; face quality assessment; performance prediction
Blasco, J.[Jorge], Chen, T.M.[Thomas M.], Tapiador, J.[Juan], Peris-Lopez, P.[Pedro],
A Survey of Wearable Biometric Recognition Systems,
Surveys(49), No. 3, December 2016, pp. Article No 43.
DOI Link
Survey, Biometrics. The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people.
Choudhury, B.[Bismita], Then, P.[Patrick], Issac, B.[Biju], Raman, V.[Valliappan], Haldar, M.K.[Manas Kumar],
A Survey on Biometrics and Cancelable Biometrics Systems,
IJIG(18), No. 01, 2018, pp. 1850006.
DOI Link
Survey, Biometrics.
Sundararajan, K.[Kalaivani], Woodard, D.L.[Damon L.],
Deep Learning for Biometrics: A Survey,
Surveys(51), No. 3, July 2018, pp. Article No 65.
DOI Link
Survey, Biometrics. Survey on the impact on biometrics.
Ribaric, S.[Slobodan], Ariyaeeinia, A.[Aladdin], Pavesic, N.[Nikola],
De-identification for privacy protection in multimedia content: A survey,
SP:IC(47), No. 1, 2016, pp. 131-151.
Elsevier DOI
Survey, Biometrics Privacy. Privacy
Maiorana, E.[Emanuele], Kalita, H.[Himanka], Campisi, P.[Patrizio],
Mobile keystroke dynamics for biometric recognition: An overview,
IET-Bio(10), No. 1, 2021, pp. 1-23.
DOI Link
Survey, Keystroke Biometrics.
Hasan, M.R.[Md Rezwan], Guest, R.[Richard], Deravi, F.[Farzin],
Presentation-Level Privacy Protection Techniques for Automated Face Recognition: A Survey,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link
Survey, Privacy. Biometrics, face detection-recognition, privacy protection techniques, facial identity hiding
Ramachandra, R.[Raghavendra], Busch, C.[Christoph],
Presentation Attack Detection Methods for Face Recognition Systems: A Comprehensive Survey,
Surveys(50), No. 1, April 2017, pp. Article No 8.
DOI Link
Survey, Spoofing. The vulnerability of face recognition systems to presentation attacks (also known as direct attacks or spoof attacks) has received a great deal of interest from the biometric community.
See also Presentation Attack Detection Methods for Fingerprint Recognition Systems: A Survey.
Rattani, A.[Ajita], Derakhshani, R.[Reza],
Ocular biometrics in the visible spectrum: A survey,
IVC(59), No. 1, 2017, pp. 1-16.
Elsevier DOI
Survey, Ocular Biometrics. Biometrics
Section, Multiple Entries: 22.5.3.7 Iris Recognition Systems, Systems, Evaluation, Comparison, Surveys Chapter Contents (Back)
Biometrics. Iris Recognition. Survey, Iris Recognition.
Wildes, R.P.,
Iris Recognition: An Emerging Biometric Technology,
PIEEE(85), No. 9, September 1997, pp. 1348-1363.
Award Paper. Survey, Iris Recognition. This paper won the 1999 Donald G. Fink award from IEEE for outstanding survey.
Wildes, R.P.[Richard P.],
Iris Recognition,
BSTDPE05(63095). Survey, Iris Recognition.
Daugman, J.G.,
How Iris Recognition Works,
CirSysVideo(14), No. 1, January 2004, pp. 21-30.
IEEE Abstract.
Earlier: ICIP02(I: 33-36).
IEEE DOI
Survey, Iris Recognition. Standard general description of Gabor filter technique.
Wayman, J., Jain, A., Maltoni, D., Maio, D., (Eds.)
Biometric Systems: Technology, Design and Performance Evaluation,
Springer2005. ISBN: 978-1-85233-596-0 Indexed as: BSTDPE05
WWW Link. Survey, Biometrics. Buy this book: Biometric Systems: Technology, Design and Performance Evaluation
Technology overviews, Iris recognition, face recognition, Speaker Verification, Assessments of fingerprint and face recognition, system design and integration, legal and privacy issues,
Bowyer, K.W.[Kevin W.], Hollingsworth, K.P.[Karen P.], Flynn, P.J.[Patrick J.],
Image Understanding for Iris Biometrics: A survey,
CVIU(110), No. 2, May 2008, pp. 281-307.
Elsevier DOI
Survey, Iris Recognition.
Ross, A.A.[Arun A.],
Iris Recognition: The Path Forward,
Computer(43), No. 2, February 2010, pp. 30-35.
IEEE DOI
Survey, Iris Recognition.
de Marsico, M.[Maria], Petrosino, A.[Alfredo], Ricciardi, S.[Stefano],
Iris recognition through machine learning techniques: A survey,
PRL(82, Part 2), No. 1, 2016, pp. 106-115.
Elsevier DOI
Survey, Iris Recognition. Biometrics
Alonso-Fernandez, F.[Fernando], Bigun, J.[Josef],
A survey on periocular biometrics research,
PRL(82, Part 2), No. 1, 2016, pp. 92-105.
Elsevier DOI
Survey, Periocular Biometrics. Periocular
Nguyen, K.[Kien], Fookes, C.[Clinton], Jillela, R.[Raghavender], Sridharan, S.[Sridha], Ross, A.[Arun],
Long range iris recognition: A survey,
PR(72), No. 1, 2017, pp. 123-143.
Elsevier DOI
Survey, Iris Recognition. Biometrics
Omelina, L.[Lubos], Goga, J.[Jozef], Pavlovicova, J.[Jarmila], Oravec, M.[Milos], Jansen, B.[Bart],
A survey of iris datasets,
IVC(108), 2021, pp. 104109.
Elsevier DOI
Survey, Iris Reognition. Dataset, Iris Recognition. Biometrics, Iris recognition, Iris datasets, Human iris
Sharma, R.[Renu], Ross, A.[Arun],
Periocular biometrics and its relevance to partially masked faces: A survey,
CVIU(226), 2023, pp. 103583.
Elsevier DOI
Survey, Periocular Biometrics. Periocular, Ocular, Biometrics
See also Face Recognition Systems, Occlusions, Masks.
Maltoni, D.[Davide], Maio, D.[Dario], Jain, A.K.[Anil K.], Prabhakar, S.[Salil],
Handbook of Fingerprint Recognition,
Springer2009. ISBN: 978-1-84882-253-5 Second Edition.
WWW Link.
Earlier: Springer-VerlagNew York, 2003
WWW Link. Survey, Fingerprints. Dataset, Fingerprints. The new edition is greatly expanded. Algorithms, evaluations, sensors, standards, security. Buy this book: Handbook of Fingerprint Recognition
Lee, H.C., Gaensslen, R.E., (Eds.)
Advances in Fingerprint Technology,
New York: Elsevier1991. Survey, Fingerprints.
Ratha, N.K.[Nalini K.], Bolle, R.M.[Ruud M.], (Eds.),
Automatic Fingerprint Recognition Systems,
Springer-Verlag2003. ISBN 0-387-95593-3,
HTML Version. Survey, Fingerprints. Buy this book: Automatic Fingerprint Recognition Systems
Chen, T.P.[Tai P.], Yau, W.Y.[Wei-Yun], Jiang, X.D.[Xu-Dong],
Token-Based Fingerprint Authentication,
RPCS(2), No. 1, January 2009, pp. 50-58.
WWW Link.
Survey, Fingerprints.
Ailisto, H.[Heikki], Lindholm, M.[Mikko], Tikkanen, P.[Pauli],
A Review Of Fingerprint Image Enhancement Methods,
IJIG(3), No. 3, July 2003, pp. 401-424.
Survey, Fingerprints.
Schuch, P.[Patrick], Schulz, S.D.[Simon-Daniel], Busch, C.[Christoph],
Survey on the impact of fingerprint image enhancement,
IET-Bio(7), No. 2, March 2018, pp. 102-115.
DOI Link
Survey, Fingerprint Enhancement.
Earlier:
De-convolutional auto-encoder for enhancement of fingerprint samples,
IPTA16(1-7)
IEEE DOI
convolution
Fei, L., Lu, G., Jia, W., Teng, S., Zhang, D.,
Feature Extraction Methods for Palmprint Recognition: A Survey and Evaluation,
SMCS(49), No. 2, February 2019, pp. 346-363.
IEEE DOI
Survey, Palmprint. Palmprint recognition, Feature extraction, Image resolution, Indexes, Image recognition, Hyperspectral imaging, high-resolution palmprint recognition
Jaswal, G.[Gaurav], Kaul, A.[Amit], Nath, R.[Ravinder],
Knuckle Print Biometrics and Fusion Schemes: Overview, Challenges, and Solutions,
Surveys(48), No. 3, February 2016, pp. 34.
DOI Link
Survey, Knuckle Prints. interface between various hand modalities, summary of inner- and dorsal-knuckle print recognition, and fusion techniques.
Section, Multiple Entries: 8.1 Segmentation, Survey and General Topics Chapter Contents (Back)
Survey, Segmentation. Segmentation, Survey.
Riseman, E.M., and Arbib, M.A.,
Computational Techniques in the Visual Segmentation of Static Scenes,
CGIP(6), No. 3, June 1977, pp. 221-276.
Elsevier DOI Survey, Segmentation. Segmentation, Survey. Segmentation, Color. Color Segmentation. Relaxation. Relaxation, Edges. Use of knowledge about scene in analysis (i.e., segmentation.); requires new structures for each type of scene (but [apparently] people don't need this.); usually boundaries are visible in intensity, but color is important; texture - hierarchical approach; boundaries; relaxation; region formation - growing, clusters, both?; labeled/unlabeled drawing; Ohlander; 2-D histogram as equivalent to 1-D histogram and not clearly explained, but...; result: combine many algorithms, redundant information/ representations (cones!), pool of features, (general system rather than specific).
Fu, K.S., Mui, J.K.,
A survey on Image Segmentation,
PR(13), No. 1, 1981, pp. 3-16.
Elsevier DOI Survey, Segmentation. Segmentation, Survey. Since little is known about how to measure segmentation, no comments on how well an algorithm works.
Haralick, R.M., and Shapiro, L.G.,
Image Segmentation Techniques,
CVGIP(29), No. 1, January 1985, pp. 100-132.
Elsevier DOI (Then at Machine Vision Intl.) Evaluation, Segmentation. Survey, Segmentation. Segmentation, Criteria. A survey of a large number of segmentation methods. Techniques include spatial clustering, thresholding, region growing, split and merge. Examples are given of various methods. Criteria for a good segmentation: uniform and homogeneous with respect to some feature. Adjacent regions should have significantly different values (w.r.t. same feature). Region interiors should be simple, not ragged, and spatially accurate.
Nevatia, R.,
Image Segmentation,
HPRIP86(215-231). USC Computer Vision Survey, Segmentation. Segmentation, Survey.
Mitiche, A.[Amar], Aggarwal, J.K.,
Image Segmentation by Conventional and Information-Integrating Techniques: A Synopsis,
IVC(3), No. 2, May 1985, pp. 50-62.
Elsevier DOI Survey, Segmentation. Survey type article that discusses segmentation in terms of combining information from several frames (mostly just general segmentation).
Gill, J.[Jasmeen], Girdhar, A.[Akshay], Singh, T.[Tejwant],
A Review of Enhancement and Segmentation Techniques for Digital Images,
IJIG(19), No. 3 2019, pp. 1950013.
DOI Link
Survey, Segmentation. Survey, Enhancement.
Wang, Y.B.[Yuan-Bo], Ahsan, U.[Unaiza], Li, H.Y.[Han-Yan], Hagen, M.[Matthew],
A Comprehensive Review of Modern Object Segmentation Approaches,
FTCGV(13), No. 2-3, 2022, pp. 111-283.
DOI Link
Survey, Segmentation.
Csurka, G.[Gabriela], Volpi, R.[Riccardo], Chidlovskii, B.[Boris],
Semantic Image Segmentation: Two Decades of Research,
FTCGV(14), No. 1-2, 2022, pp. 1-162.
DOI Link
Survey, Semantic Segmentation.
Ranade, S., and Prewitt, J.M.S.,
A Comparison of Some Segmentation Algorithms for Cytology,
ICPR80(561-564). Survey, Segmentation. Evaluation, Segmentation. Segmentation, Histogram. Segmentation, Survey. Relaxation. Segmentation, Evaluation. Compares 5 segmentation method for cell segmentation: Histogram based threshold selection. Adequate and best overall, computationally simple. Probably:
See also Object Enhancement and Extraction. Mode sharpening before 1. Applied to the histogram not an image mode filter. Problems: it is a global transformation. Region/edge:
See also Region Extraction Using Convergent Evidence. spurious edges in texture cause problems. Quad-tree (edge-region): local, sensitive to choice of split criterion and threshold selections. General papers:
See also Segmentation by Split and Merge Techniques, Hierarchical. Relaxation (
See also Scene Labeling by Relaxation Operations. ): sensitive to compatibility measure.
Weszka, J.S.[Joan S.],
A Survey of Threshold Selection Techniques,
CGIP(7), No. 2, April 1978, pp. 259-265.
Elsevier DOI Threshold Selection, Survey. Survey, Segmentation. Survey, Threshold Selection. Segmentation, Thresholds. Segmentation, Survey.
Sahoo, P.K., Soltani, S., Wong, A.K.C.,
A Survey of Thresholding Techniques,
CVGIP(41), No. 2, February 1988, pp. 233-260.
Elsevier DOI Survey, Segmentation. Survey, Threshold Selection. Segmentation, Thresholds. Threshold Selection, Survey. An update of
See also Survey of Threshold Selection Techniques, A. Analytic analysis of thresholding results.
Pal, N.R., and Pal, S.K.,
A Review on Image Segmentation Techniques,
PR(26), No. 9, September 1993, pp. 1277-1294.
Elsevier DOI
PDF File. Survey, Segmentation. Evaluation, Segmentation.
Zhang, Y.J.,
A Survey on Evaluation Methods for Image Segmentation,
PR(29), No. 8, August 1996, pp. 1335-1346.
Elsevier DOI
Evaluation, Segmentation. Survey, Segmentation. Segmentation, Evaluation.
Chang, C.I., Du, Y., Wang, J., Guo, S.M., Thouin, P.D.,
Survey and comparative analysis of entropy and relative entropy thresholding techniques,
VISP(153), No. 6, December 2006, pp. 837-850.
DOI Link
Survey, Segmentation.
Zhang, H.[Hui], Fritts, J.E.[Jason E.], Goldman, S.A.[Sally A.],
Image segmentation evaluation: A survey of unsupervised methods,
CVIU(110), No. 2, May 2008, pp. 260-280.
Elsevier DOI
Evaluation, Segmentation. Survey, Segmentation. Image segmentation; Objective evaluation; Unsupervised evaluation; Empirical goodness measure
Freixenet, J., Muñoz, X., Raba, D., Martí, J., Cufí, X.,
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration,
ECCV02(III: 408 ff.).
Springer DOI
Survey, Segmentation. Edge and region integration approaches.
Schachter, B.J.,
A Survey and Evaluation of FLIR Target Detection/Segmentation Algorithms,
DARPA82(49-57). Survey, Segmentation. ATR. FLIR.
Han, J.W.[Jun-Wei], Zhang, D.W.[Ding-Wen], Cheng, G.[Gong], Liu, N.[Nian], Xu, D.[Dong],
Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey,
SPMag(35), No. 1, January 2018, pp. 84-100.
IEEE DOI
Survey, Deep Nets. Computer architecture, Convolution, Feature extraction, Machine learning, Object detection, Visualization
Guo, Y.M.[Yan-Ming], Liu, Y.[Yu], Georgiou, T.[Theodoros], Lew, M.S.[Michael S.],
A review of semantic segmentation using deep neural networks,
MultInfoRetr(8), No. 2, June 2018, pp. 87-93.
Springer DOI
Survey, Semantic Segmentation.
Zhang, Y.F.[Yi-Fei], Sidibé, D.[Désiré], Morel, O.[Olivier], Mériaudeau, F.[Fabrice],
Deep multimodal fusion for semantic image segmentation: A survey,
IVC(105), 2021, pp. 104042.
Elsevier DOI
Survey, Semantic Segmentation. Image fusion, Multi-modal, Deep learning, Semantic segmentation
Prati, A.[Andrea], Mikic, I.[Ivana], Trivedi, M.M.[Mohan M.], Cucchiara, R.[Rita],
Detecting moving shadows: algorithms and evaluation,
PAMI(25), No. 7, July 2003, pp. 918-923.
IEEE Abstract.
WWW Link.
Evaluation, Shadow Detection. Survey, Shadow Detection. Classify techniques as either statistical or deterministic. Statistical Nonparametric (
See also Statistical Approach for Real-time Robust Background Subtraction and Shadow Detection, A. or
See also Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. ) Statistical Parametric (
See also Moving Shadow and Object Detection in Traffic Scenes. ) Deterministic non-model (
See also Detection of Moving Cast Shadows for Object Segmentation. and
See also Detecting Moving Objects, Ghosts, and Shadows in Video Streams. )
Sanin, A.[Andres], Sanderson, C.[Conrad], Lovell, B.C.[Brian C.],
Shadow detection: A survey and comparative evaluation of recent methods,
PR(45), No. 4, 2012, pp. 1684-1695.
Elsevier DOI
Award, Pattern Recognition, Honorable Mention. Survey, Shadow Detection. Moving cast shadow detection
Zucker, S.W.[Steven W.],
Region Growing: Childhood and Adolescence,
CGIP(5), No. 3, September 1976, pp. 382-399.
Elsevier DOI Survey, Segmentation. Segmentation, Survey.
Peng, B.[Bo], Zhang, L.[Lei], Zhang, D.[David],
A survey of graph theoretical approaches to image segmentation,
PR(46), No. 3, March 2013, pp. 1020-1038.
Elsevier DOI
Survey, Segmentation. Image segmentation; Graph theoretical methods; Minimal spanning tree; Graph cut
Wang, M.[Murong], Liu, X.B.[Xia-Bi], Gao, Y.X.[Yi-Xuan], Ma, X.[Xiao], Soomro, N.Q.[Nouman Q.],
Superpixel segmentation: A benchmark,
SP:IC(56), No. 1, 2017, pp. 28-39.
Elsevier DOI
Survey, Superpixel Segmentation. Superpixel
Stutz, D.[David], Hermans, A.[Alexander], Leibe, B.[Bastian],
Superpixels: An evaluation of the state-of-the-art,
CVIU(166), No. 1, 2018, pp. 1-27.
Elsevier DOI
Survey, Superpixels. Superpixels
Ren, Y.[Yan], Kong, A.W.K.[Adams Wai Kin], Jiao, L.C.[Li-Cheng],
A survey on image and video cosegmentation: Methods, challenges and analyses,
PR(103), 2020, pp. 107297.
Elsevier DOI
Survey, Co-Segmentation. Image cosegmentation, Video cosegmentation
El Jurdi, R.[Rosana], Petitjean, C.[Caroline], Honeine, P.[Paul], Cheplygina, V.[Veronika], Abdallah, F.[Fahed],
High-level prior-based loss functions for medical image segmentation: A survey,
CVIU(210), 2021, pp. 103248.
Elsevier DOI
Survey, Segmentation. Survey, Medical. Prior-based loss functions, Anatomical constraint losses, Convolutional neural networks, Medical image segmentation, Deep learning
Wang, R.S.[Ri-Sheng], Lei, T.[Tao], Cui, R.X.[Rui-Xia], Zhang, B.T.[Bing-Tao], Meng, H.Y.[Hong-Ying], Nandi, A.K.[Asoke K.],
Medical image segmentation using deep learning: A survey,
IET-IPR(16), No. 5, 2022, pp. 1243-1267.
DOI Link
Survey, Medical Images.
He, L.[Lei], Peng, Z.G.[Zhi-Gang], Everding, B.[Bryan], Wang, X.[Xun], Han, C.Y.[Chia Y.], Weiss, K.L.[Kenneth L.], Wee, W.G.[William G.],
A comparative study of deformable contour methods on medical image segmentation,
IVC(26), No. 2, 1 February 2008, pp. 141-163.
Elsevier DOI
Survey, Snakes. Medical image segmentation; Deformable contour method; Snake; Level set; Comparative study
Heo, G.[Giseon], Small, C.G.[Christopher G.],
Form representions and means for landmarks: A survey and comparative study,
CVIU(102), No. 2, May 2006, pp. 188-203.
Elsevier DOI Survey, Segmentation. Tomography; Invariance
Zhao, F.[Feng], Xie, X.H.[Xiang-Hua],
An Overview of Interactive Medical Image Segmentation,
BMVA(2013), No. 1, 2013, pp. 7, 1-22.
PDF File.
Survey, Segmentation.
Mitiche, A.[Amar], Ben Ayed, I.[Ismail],
Variational and Level Set Methods in Image Segmentation,
Springer2011, ISBN: 978-3-642-15351-8
WWW Link. Survey, Level Set Segmentation. Buy this book: Variational and Level Set Methods in Image Segmentation (Springer Topics in Signal Processing)
Code, Level Set Segmentation. Code:
WWW Link.
Biswas, S.[Sambhunath], Lovell, B.C.[Brian C.],
Bézier and Splines in Image Processing and Machine Vision,
Springer2008, ISBN: 978-1-84628-956-9.
WWW Link. Survey, Splines. Survey, Active Contours.
Section, Multiple Entries: 8.8.6 Texture Segmentation, Surveys, Review and General Chapter Contents (Back)
Segmentation, Texture. Survey, Segmentation. Survey, Texture.
Reed, T.R., and du Buf, J.M.H.,
A Review of Recent Texture Segmentation and Feature Extraction Techniques,
CVGIP(57), No. 3, May 1993, pp. 359-372.
DOI Link Survey, Texture.
Zhang, R., Tsai, P.S., Cryer, J.E., Shah, M.,
Shape from Shading: A Survey,
PAMI(21), No. 8, August 1999, pp. 690-706.
IEEE DOI Survey, Shape from Shading. Minimization:
See also Estimation of Illuminant Direction, Albedo, and Shape from Shading.
See also Shape from Shading with a Linear Triangular Element Surface Model. Propagation:
See also Simple Algorithm for Shape for Shading, A. Local:
See also Improved Methods of Estimating Shape from Shading Using the Light Source Coordinate System. Linear:
See also Shape Information from Shading: A Theory about Human Perception. (Would
See also On the Extraction of Shape Information from Shading. be better reference?)
See also Shape From Shading Using Linear-Approximation. (The text says 1992, but that is the conference reference) Code, Shape from Shading.
WWW Link.
Durou, J.D.[Jean-Denis], Falcone, M.[Maurizio], Sagona, M.[Manuela],
Numerical methods for shape-from-shading: A new survey with benchmarks,
CVIU(109), No. 1, January 2008, pp. 22-43.
Elsevier DOI
Survey, Shape from Shading. Shape-from-shading; Eikonal equation; Numerical methods; Algorithms comparison
Quéau, Y.[Yvain], Durou, J.D.[Jean-Denis], Aujol, J.F.[Jean-François],
Normal Integration: A Survey,
JMIV(60), No. 4, May 2018, pp. 576-593.
Springer DOI
Survey, Shape from Shading. From normals to surfaces. Part of all SfS work.
CVOnline: Photometric Stereo,
CV-Online2002.
WWW Link. Survey, Photometric Stereo.
Ackermann, J.[Jens], Goesele, M.[Michael],
A Survey of Photometric Stereo Techniques,
FTCGV(9), No. 3-4, 2015, pp. 149-254.
DOI Link
Survey, Photometric Stereo.
Deeb, R.[Rada], Muselet, D.[Damien], Hebert, M.[Mathieu], Tremeau, A.[Alain],
Interreflections in Computer Vision: A Survey and an Introduction to Spectral Infinite-Bounce Model,
JMIV(60), No. 5, June 2018, pp. 661-680.
Springer DOI
Survey, Interreflections.
Batlle, J., Mouaddib, E.[El_Mustapha], Salvi, J.,
Recent Progress in Coded Structured Light as a Technique to Solve the Correspondence Problem: A Survey,
PR(31), No. 7, July 1998, pp. 963-982.
Elsevier DOI
Survey, Structured Light.
Lau, D.[Daniel],
3-D Imaging Advances Capabilities of Machine Vision: Part I,
VisSys(17), No. 4, April 2012, pp. 19-21.
HTML Version. Survey, 3-D Imaging.
And:
3-D Imaging Advances Capabilities of Machine Vision: Part II,
VisSys(17), No. 5, May 2012, pp. 19-22.
HTML Version.
And:
3-D Imaging Advances Capabilities of Machine Vision: Part III,
VisSys(17), No. 6, June 2012.
HTML Version. How do structured-light systems work and what are the advantages over other 3-D imaging systems?
Wilson, A.[Andrew],
Scanning Sensors,
VisSys(15), No. 12, December 2010. Survey, Structured Light.
HTML Version.
Measuring the three-dimensional shape of complex objects allows shape and volume measurements to be more easily attained by robotic systems
Thiebaut, E.[Eric], Young, J.[John],
Principles of image reconstruction in optical interferometry: tutorial,
JOSA-A(34), No. 6, June 2017, pp. 904-923.
DOI Link
Survey, Optical Interferometry. Image reconstruction-restoration, Inverse problems, Interferometric, imaging
Han, X.F.[Xian-Feng], Laga, H.[Hamid], Bennamoun, M.[Mohammed],
Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era,
PAMI(43), No. 5, May 2021, pp. 1578-1604.
IEEE DOI
Survey, 3D Reconstruction. Image reconstruction, Shape, Training, Deep learning, 3D video
Ehret, T.[Thibaud],
Monocular Depth Estimation: a Review of the 2022 State of the Art,
IPOL(13), 2023, pp. 38-56.
DOI Link
Survey, Monocular Depth.
Arampatzakis, V.[Vasileios], Pavlidis, G.[George], Mitianoudis, N.[Nikolaos], Papamarkos, N.[Nikos],
Monocular Depth Estimation: A Thorough Review,
PAMI(46), No. 4, April 2024, pp. 2396-2414.
IEEE DOI
Survey, Monocular Depth. Estimation, Visualization, Observers, Pattern analysis, Visual perception, Real-time systems, Artificial intelligence, monocular depth
Marshall, D.[Dave],
CVOnline: Polyhedral World Line Labeling,
CV-Online1997.
HTML Version. Survey, Line Labeling.
Owens, R.[Robyn],
CVOnline: Line drawings and line labeling,
CV-Online1997.
HTML Version. Survey, Line Labeling.
Smith, R.W.[Raymond W.],
Computer Processing of Line Images: A Survey,
PR(20), No. 1, 1987, pp. 7-15.
Elsevier DOI Survey, Line Drawings.
Bénard, P.[Pierre], Hertzmann, A.[Aaron],
Line Drawings from 3D Models: A Tutorial,
FTCGV(11), No. 1-2, 2019, pp. 1-159.
DOI Link
Survey, Line Drawings.
Garces, E.[Elena], Rodriguez-Pardo, C.[Carlos], Casas, D.[Dan], Lopez-Moreno, J.[Jorge],
A Survey on Intrinsic Images: Delving Deep into Lambert and Beyond,
IJCV(130), No. 3, March 2022, pp. 836-868.
Springer DOI
Survey, Intrinsic Images.
CVOnline: Multi-Sensor, Multi-View Geometries,
CV-Online2006.
WWW Link. Survey, Stereo.
Barnard, S.T.[Stephen T.], and Fischler, M.A.[Martin A.],
Computational Stereo,
Surveys(14), No. 4, December 1982, pp. 553-572. Survey, Stereo.
Earlier:
Computational Stereo from an IU Perspective,
DARPA81(157-167).
And: an update:
Computational and Biological Models of Stereo Vision,
DARPA90(439-448). Stereo. The paper is a survey of many stereo systems from the Image Understanding community: CMU, CDC, Lockheed, MIT, SRI, UMinn, Stanford. There is also a brief review of the stereo problem.
Brown, M.Z.[Myron Z.], Burschka, D.[Darius], Hager, G.D.[Gregory D.],
Advances in Computational Stereo,
PAMI(25), No. 8, August 2003, pp. 993-1008.
IEEE Abstract.
Survey, Stereo. Survey of the advances in Correspondence, Occlusion, Real Time.
Laga, H.[Hamid], Jospin, L.V.[Laurent Valentin], Boussaid, F.[Farid], Bennamoun, M.[Mohammed],
A Survey on Deep Learning Techniques for Stereo-Based Depth Estimation,
PAMI(44), No. 4, April 2022, pp. 1738-1764.
IEEE DOI
Survey, Stereo. Estimation, Videos, Deep learning, Australia, Training, Pipelines, CNN, deep learning, 3D reconstruction, stereo matching, feature matching
Hirschmuller, H.[Heiko], Scharstein, D.[Daniel],
Evaluation of Stereo Matching Costs on Images with Radiometric Differences,
PAMI(31), No. 9, September 2009, pp. 1582-1599.
IEEE DOI
Survey, Stereo Matching.
Earlier:
Evaluation of Cost Functions for Stereo Matching,
CVPR07(1-8).
IEEE DOI
Evaluation w.r.t. radiometric changes. Pixel and window Bilateral Background Subtraction is among the best. Hierarchical Mutual Information (HMI) also good.
Scharstein, D.[Daniel], Szeliski, R.S.[Richard S.],
Middlebury stereo vision page,
Online2007
WWW Link. Survey, Stereo.
Tippetts, B.J.[Beau J.], Lee, D.J.[Dah Jye], Lillywhite, K.[Kirt], Archibald, J.K.[James K.],
Review of stereo vision algorithms and their suitability for resource-limited systems,
RealTimeIP(11), No. 1, January 2016, pp. 5-25.
Springer DOI
Survey, Stereo.
Kak, A.C.,
Depth Perception for Robots,
HIR841984, pp. XX-YY.
And: Purdue-TR-83-44, October, 1983. Stereo. Survey, Stereo. A survey article that describes the stereo computations and some of the solutions that are available.
Szeliski, R.S.[Richard S.], Zabih, R.[Ramin], Scharstein, D.[Daniel], Veksler, O.[Olga], Kolmogorov, V.[Vladimir], Agarwala, A.[Aseem], Tappen, M.[Marshall], Rother, C.[Carsten],
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors,
PAMI(30), No. 6, June 2008, pp. 1068-1080.
IEEE DOI
Survey, Energy Minimization.
Earlier:
A Comparative Study of Energy Minimization Methods for Markov Random Fields,
ECCV06(II: 16-29).
Springer DOI
Iterated conditional Modes (ICM) (
See also On the Statistical Analysis of Dirty Pictures. ), Graph Cuts (
See also Fast Approximate Energy Minimization via Graph Cuts. ), Max-Product Loopy Belief Propogation LBP (
See also Efficient Belief Propagation for Early Vision. ) or (
See also Learning Low-Level Vision. ) Tree Reweighted Message Passing (TRW) (Similar to LBP, but different) (
See also MAP Estimation via Agreement on (Hyper)Trees: Message-Passing and Linear-Programming Approaches. )
Furukawa, Y.[Yasutaka], Hernández, C.[Carlos],
Multi-View Stereo: A Tutorial,
FTCGV(9), No. 1-2, 2015, pp. 1-148.
DOI Link Survey, Stereo, Multi-View.
Sahin, E.[Erdem], Stoykova, E.[Elena], Makinen, J.[Jani], Gotchev, A.[Atanas],
Computer-Generated Holograms for 3D Imaging: A Survey,
Surveys(53), No. 2, March 2020, pp. xx-yy.
DOI Link
Survey, Holograms. 3D displays, Computer-generated holograms, 3D imaging
Pastoor, S.[Siegmund],
3D-television: A survey of recent research results on subjective requirements,
SP:IC(4), No. 1, November 1991, pp. 21-32.
Elsevier DOI
Survey, Display. Screen size; viewing distance; accommodation; irrelevance reduction; spatio-temporal resolution; disparity; motion parallax; noise interference; interchannel asymmetry
Smolic, A., Kauff, P.,
Interactive 3-D Video Representation and Coding Technologies,
PIEEE(93), No. 1, January 2005, pp. 98-110.
IEEE DOI
Survey, Stereo Display.
Schwarz, S., Olsson, R., Sjostrom, M.,
Depth Sensing for 3DTV: A Survey,
MultMedMag(20), No. 4, October 2013, pp. 10-17.
IEEE DOI
Survey, 3DTV. rendering (computer graphics)
Terzic, K.[Kasim], Hansard, M.[Miles],
Methods for reducing visual discomfort in stereoscopic 3D: A review,
SP:IC(47), No. 1, 2016, pp. 402-416.
Elsevier DOI
Survey, Stereo Displays. Stereoscopic 3D
Bermejo, C.[Carlos], Hui, P.[Pan],
A Survey on Haptic Technologies for Mobile Augmented Reality,
Surveys(54), No. 9, October 2021, pp. xx-yy.
DOI Link
Survey, Haptic Technology. haptic feedback, Mobile augmented reality, interactions, haptic devices
CVonline: Geometric Feature Extraction Methods,
CV-OnlineJuly 2001.
HTML Version. Survey, Feature Extraction.
Bayro-Corrochano, E.[Eduardo],
Geometric Computing for Wavelet Transforms, Robot Vision, Learning, Control and Action,
Springer-Verlag2010.I ISBN: 978-1-84882-928-2
WWW Link. Survey, Feature Computation. Buy this book: Geometric Computing: for Wavelet Transforms, Robot Vision, Learning, Control and Action
Various features, geometric computing, FFT, wavelets, graphics.
Georgiou, T.[Theodoros], Liu, Y.[Yu], Chen, W.[Wei], Lew, M.[Michael],
A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision,
MultInfoRetr(9), No. 3, September 2020, pp. 135-170.
Springer DOI
Survey, Descriptions.
Carreira-Perpinan, M.A.,
A review of mean-shift algorithms for clustering,
Online2015.
WWW Link. Survey, Mean Shift.
See also Implementation of the Mean Shift Algorithm, An.
Zhang, X.[Xin], Yang, Y.H.[Yee-Hong], Han, Z.G.[Zhi-Guang], Wang, H.[Hui], Gao, C.[Chao],
Object class detection: A survey,
Surveys(46), No. 1, October 2013, pp. Article No 10.
DOI Link
Survey, Object Class. Object class detection, also known as category-level object detection, has become one of the most focused areas in computer vision in the new century. This article attempts to provide a comprehensive survey of the recent technical achievements.
Rahman, M.M., Tan, Y., Xue, J., Lu, K.,
Recent Advances in 3D Object Detection in the Era of Deep Neural Networks: A Survey,
IP(29), 2020, pp. 2947-2962.
IEEE DOI
Survey, Objetc Detection. Object detection, Cameras, Sensors, Laser radar, Task analysis, deep learning
Oksuz, K.[Kemal], Cam, B.C.[Baris Can], Kalkan, S.[Sinan], Akbas, E.[Emre],
Imbalance Problems in Object Detection: A Review,
PAMI(43), No. 10, October 2021, pp. 3388-3415.
IEEE DOI
Survey, Object Detection.
Earlier: A1, A2, A4, A3:
Generating Positive Bounding Boxes for Balanced Training of Object Detectors,
WACV20(883-892)
IEEE DOI
Object detection, Taxonomy, Feature extraction, Deep learning, Pipelines, Neural networks, Pattern analysis, Object detection, objective imbalance. Generators, Detectors, Training, Object detection, Sampling methods, Pipelines, Proposals
Zou, Z.X.[Zheng-Xia], Chen, K.[Keyan], Shi, Z.W.[Zhen-Wei], Guo, Y.H.[Yu-Hong], Ye, J.P.[Jie-Ping],
Object Detection in 20 Years: A Survey,
PIEEE(111), No. 3, March 2023, pp. 257-276.
IEEE DOI
Survey, Object Detection. Object detection, Detectors, Feature extraction, Deep learning, Convolutional neural networks, technical evolution
Liu, L.[Li], Ouyang, W.L.[Wan-Li], Wang, X.G.[Xiao-Gang], Fieguth, P.W.[Paul W.], Chen, J.[Jie], Liu, X.W.[Xin-Wang], Pietikäinen, M.[Matti],
Deep Learning for Generic Object Detection: A Survey,
IJCV(128), No. 2, February 2020, pp. 261-318.
Springer DOI
Survey, Generic Object Detecion.
Huang, G.[Gabriel], Laradji, I.[Issam], Vázquez, D.[David], Lacoste-Julien, S.[Simon], Rodríguez, P.[Pau],
A Survey of Self-Supervised and Few-Shot Object Detection,
PAMI(45), No. 4, April 2023, pp. 4071-4089.
IEEE DOI
Survey, Few-Shot Object Detection. Object detection, Detectors, Transformers, Feature extraction, Task analysis, Head, Benchmark testing, Self-supervised, few-shot, instance segmentation
Sheng, Y.L.[Yun-Long], Sahli, S.[Samir], Ouyang, Y.[Yueh], Lavigne, D.[Daniel],
Object detection: From optical correlator to intelligent recognition surveillance system,
SPIE(Newsroom), October 21, 2013.
DOI Link
Survey, Object Detection. Component- and feature-based approaches enable object detection in aerial imagery.
Li, Z.[Zheng], Wang, Y.C.[Yong-Cheng], Zhang, N.[Ning], Zhang, Y.X.[Yu-Xi], Zhao, Z.K.[Zhi-Kang], Xu, D.D.[Dong-Dong], Ben, G.L.[Guang-Li], Gao, Y.X.[Yun-Xiao],
Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link
Survey, Object Detection.
Tuytelaars, T.[Tinne], Mikolajczyk, K.[Krystian],
Local Invariant Feature Detectors: A Survey,
FTCGV(3), Issue 3, 2007, pp. 177-280.
DOI Link
Survey, Features. Published June 2008.
Cheng, G.[Gong], Yuan, X.[Xiang], Yao, X.[Xiwen], Yan, K.[Kebing], Zeng, Q.H.[Qing-Hua], Xie, X.X.[Xing-Xing], Han, J.W.[Jun-Wei],
Towards Large-Scale Small Object Detection: Survey and Benchmarks,
PAMI(45), No. 11, November 2023, pp. 13467-13488.
IEEE DOI
Survey, Small Object Detection.
Section, Multiple Entries: 7.2.1.1 Shape Computation Surveys, Comparisons Chapter Contents (Back)
Geometric Features. Features, Geometric. Shape, 2D. Survey, Shape.
Pavlidis, T.[Theodosios],
A Review of Algorithms for Shape Analysis,
CGIP(7), No. 2, April 1978, pp. 243-258.
Elsevier DOI Survey, Shape. Shape, Survey. Moments. Fourier Descriptors. Internal Scaler Transform, Moments; 2DFFT, binary masks; External Scaler Transform, FT of boundary; Internal Space Domain, medial axis transform (MAT), integral geometry, analysis with random chords, decomposition, convexity, primary convex subsets (PCS), polygonal approximations-divide at concave angles; external space domain chain codes, curvature/corners, syntactic;
Pavel, M.[Monique],
Algebraic, topological and categorical aspects of pattern recognition: A survey,
PR(14), No. 1-6, 1981, pp. 117-120.
Elsevier DOI
Survey, Pattern Recognition.
Brimkov, V.E.[Valentin E.], Barneva, R.P.[Reneta P.], (Eds.)
Digital Geometry Algorithms,
Springer2012, ISBN: 978-94-007-4173-7
 
|
WWW Link.
Survey, Digital Geometry.
Kong, T.Y., Rosenfeld, A.,
Digital Topology: Introduction and Survey,
CVGIP(48), No. 3, December 1989, pp. 357-393.
Elsevier DOI Survey, Digital Topology. Digital Topology, Survey. Study of properties of image arrays to provide a basis for image processing operations.
See also Three-Dimensional Digital Topology.
McAndrew, A., Osborne, C.,
A Survey of Algebraic Methods in Digital Topology,
JMIV(6), No. 2-3, June 1996, pp. 139-159.
Survey, Digital Topology.
Pavlidis, T.,
Algorithms for Shape Analysis of Contours and Waveforms,
PAMI(2), No. 4, July 1980, pp. 301-312. Survey, Shape. Shape, Survey.
Earlier:
Algorithms for the Shape Analysis of Contours and Waveforms,
ICPR78(70-85).
Lam, L., Lee, S.W., Suen, C.Y.,
Thinning Methodologies: A Comprehensive Survey,
PAMI(14), No. 9, September 1992, pp. 869-885.
IEEE DOI Survey, Thinning. Thinning Techniques. Thinning, skeletons, parallel and sequential techniques.
Fabbri, R.[Ricardo], da Fontoura Costa, L.[Luciano], Torelli, J.C.[Julio C.], Bruno, O.M.[Odemir M.],
2D Euclidean distance transform algorithms: A comparative survey,
Surveys(40), No. 1, February 2008, pp. 1-44.
WWW Link.
Survey, Distance Measures.
Ramanan, D.[Deva], Baker, S.[Simon],
Local Distance Functions: A Taxonomy, New Algorithms, and an Evaluation,
PAMI(33), No. 4, April 2011, pp. 794-806.
IEEE DOI
Earlier: ICCV09(301-308).
IEEE DOI
Survey, Distance Functions. Classify by how, where and when they estimate geodesic distance defined by the metric tensor.
Xavier, E.M.A.[Emerson M. A.], Ariza-López, F.J.[Francisco J.], Ureña-Cámara, M.A.[Manuel A.],
A Survey of Measures and Methods for Matching Geospatial Vector Datasets,
Surveys(48), No. 3, February 2016, pp. 39.
DOI Link
Survey, Geospatial Matching. Survey of procedures to find the correspondences between two vector datasets and similarity measures.
d'Amico, M.[Michele], Frosini, P.[Patrizio], Landi, C.[Claudia],
Using matching distance in size theory: A survey,
IJIST(16), No. 5, 2006, pp. 154-161.
DOI Link
Survey, Distance.
Saha, P.K.[Punam K.], Borgefors, G.[Gunilla], Sanniti di Baja, G.[Gabriella],
A survey on skeletonization algorithms and their applications,
PRL(76), No. 1, 2016, pp. 3-12.
Elsevier DOI
Survey, Skeletonization. Skeletonization
He, L.F.[Li-Feng], Ren, X.[Xiwei], Gao, Q.H.[Qi-Hang], Zhao, X.[Xiao], Yao, B.[Bin], Chao, Y.Y.[Yu-Yan],
The connected-component labeling problem: A review of state-of-the-art algorithms,
PR(70), No. 1, 2017, pp. 25-43.
Elsevier DOI
Survey, Connected Components. Connected-component, labeling
Aurenhammer, F.[Franz],
Voronoi Diagrams: A Survey of a Fundamental Geometric Data Structure,
Surveys(23), No. 3, September 1991, pp. 345-405. Survey, Voronoi.
Attene, M.[Marco], Campen, M.[Marcel], Kobbelt, L.[Leif],
Polygon mesh repairing: An application perspective,
Surveys(45), No. 2, February 2013, pp. Article No 15.
DOI Link
Survey, Mesh.
Samet, H.,
The Quadtree and Related Hierarchical Data Structures,
Surveys(16), No. 2, June, 1984, pp. 187-260. Quadtree, Survey. Survey, Data Structures. Survey, Quadtree. (UMd) The paper to define and explor quadtrees.
Bentley, J.L., Friedman, J.H.,
Data Structures for Range Searching,
Surveys(11), No. 4, December 1979, pp. 397-409. Survey, Data Structures.
Samet, H.[Hanan],
Hierarchical Representations of Collections of Small Rectangles,
Surveys(20), No. 4, December, 1988, pp. 271-309. Survey, Data Structures. Extension of the quadtree idea to general rectangles.
Ahuja, N., Schacter, B.,
Image Models,
Surveys(13), No. 4, December 1981, pp. 373-397.
And: Comments: Surveys(15), No. 1, March 1983, pp. 83-84. Survey, Texture. Mostly about texture representation.
Wechsler, H.,
Texture Analysis: A Survey,
SP(2), 1980, 271-282. Survey, Texture.
Van Gool, L.J., Dewaele, P., Oosterlinck, A.,
Texture Analysis Anno 1983,
CVGIP(29), No. 3, 1985, pp. 336-357.
Elsevier DOI Survey, Texture. Texture, Survey. A recent review of texture analysis methods. Statistical and structural methods. Gray level difference method, filter mask texture measures, Fourier power spectrum analysis, cooccurrence features, gray level run lengths, autocorrelation features, methods derived from texture models, relative extrema measures, and gray level profiles.
Tomita, F., Tsuji, S.,
Computer Analysis of Visual Textures,
Hingham, MA: KluwerAcademic, August 1990. ISBN 0-7923-9114-4. Survey of theories and techniques for texture analysis.
WWW Link. Survey, Texture.
Haralick, R.M.,
Statistical and Structural Approaches to Texture,
PIEEE(67), No. 5, May 1979, pp. 786-804.
Earlier: ICPR78(45-69). Survey, Texture. Texture, Survey. A good review of texture.
Mirmehdi, M.[Majid], Xie, X.H.[Xiang-Hua], Suri, J.[Jasjit],
Handbook of Texture Analysis,
World Scientific2008. ISBN 978-1-84816-115-3. Survey, Texture. Collects the basic texture approaches in one book. Buy this book: Handbook of Texture Analysis
Hossain, S.[Shahera], Serikawa, S.[Seiichi],
Texture databases: A comprehensive survey,
PRL(34), No. 15, 2013, pp. 2007-2022.
Elsevier DOI
Dataset, Texture. Survey, Texture Datasets. Texture.
Filip, J.[Jiri], Haindl, M.[Michal],
Bidirectional Texture Function Modeling: A State of the Art Survey,
PAMI(31), No. 11, November 2009, pp. 1921-1940.
IEEE DOI
Survey, Texture.
Wang, C.H.[Chao-Hui], Komodakis, N.[Nikos], Paragios, N.[Nikos],
Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey,
CVIU(117), No. 11, 2013, pp. 1610-1627.
Elsevier DOI
Survey, Markov Random Fields. Markov Random Fields
Dong, W.M.[Wei-Ming], Paul, J.C.[Jean-Claude],
Review on Recent Patents in Texture Synthesis,
RPCS(2), No. 2, June 2009, pp. 111-115.
WWW Link. Survey, Texture Synthesis.
Xie, X.H.[Xiang-Hua],
A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques,
ELCVIA(7), No. 3, 2008, pp. 1-22.
DOI Link
Survey, Defect Detection.
Total found: 1303