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The most cited papers in Computer Vision

In Computer Vision, Paper Talk on February 10, 2012 at 11:10 pm

by gooly (Li Yang Ku)

Although it’s not always the case that a paper cited more contributes more to the field, a highly cited paper usually indicates that something interesting have been discovered. The following are the papers to my knowledge being cited the most in Computer Vision. (updated on 11/24/2013) If you want your “friend’s” paper listed here, just comment below.

Cited by 21528 + 6830 (Object recognition from local scale-invariant features)

Distinctive image features from scale-invariant keypoints

DG Lowe – International journal of computer vision, 2004

Cited by 17671

A theory for multiresolution signal decomposition: The wavelet representation

SG Mallat – Pattern Analysis and Machine Intelligence, IEEE …, 1989

Cited by 17611

A computational approach to edge detection

J Canny – Pattern Analysis and Machine Intelligence, IEEE …, 1986

Cited by 15422

Snakes: Active contour models

M Kass, A Witkin, Demetri Terzopoulos – International journal of computer …, 1988

Cited by 15188

Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images

Geman and Geman – Pattern Analysis and Machine …, 1984

Cited by 11630+ 4138 (Face Recognition using Eigenfaces)

Eigenfaces for Recognition

Turk and Pentland, Journal of cognitive neuroscience Vol. 3, No. 1, Pages 71-86, 1991 (9358 citations)

Cited by 8788

Determining optical flow

B.K.P. Horn and B.G. Schunck, Artificial Intelligence, vol 17, pp 185-203, 1981

Cited by 8559

Scale-space and edge detection using anisotropic diffusion

P Perona, J Malik

Pattern Analysis and Machine Intelligence, IEEE Transactions on 12 (7), 629-639

Cited by 8432 + 5901 (Robust real time face detection)

Rapid object detection using a boosted cascade of simple features

P Viola, M Jones

Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the …

Cited by 7517

An iterative image registration technique with an application to stereo vision

B. D. Lucas and T. Kanade (1981), An iterative image registration technique with an application to stereo vision. Proceedings of Imaging Understanding Workshop, pages 121–130

Cited by 7979

Normalized cuts and image segmentation

J Shi, J Malik

Pattern Analysis and Machine Intelligence, IEEE Transactions on 22 (8), 888-905

Cited by 6658

Histograms of oriented gradients for human detection

N Dalal… – … 2005. CVPR 2005. IEEE Computer Society …, 2005

Cited by 6528

Mean shift: A robust approach toward feature space analysis

D Comaniciu, P Meer – … Analysis and Machine Intelligence, …, 2002

Cited by 5130

The Laplacian pyramid as a compact image code

Burt and Adelson, – Communications, IEEE Transactions on, 1983

Cited by 4870

Condensation—conditional density propagation for visual tracking

M Isard and Blake – International journal of computer vision, 1998

Cited by 4884

Good features to track

Shi and Tomasi , 1994. Proceedings CVPR’94., 1994 IEEE, 1994

Cited by 4875

A model of saliency-based visual attention for rapid scene analysis

L Itti, C Koch, E Niebur, Analysis and Machine Intelligence, 1998

Cited by 4769

A performance evaluation of local descriptors

K Mikolajczyk, C Schmid

Pattern Analysis and Machine Intelligence, IEEE Transactions on 27 (10 ..

Cited by 4070

Fast approximate energy minimization via graph cuts

Y Boykov, O Veksler, R Zabih

Pattern Analysis and Machine Intelligence, IEEE Transactions on 23 (11 .

Cited by 3634

Surf: Speeded up robust features

H Bay, T Tuytelaars… – Computer Vision–ECCV 2006, 2006

Cited by 3702

Neural network-based face detection

HA Rowley, S Baluja, Takeo Kanade – Pattern Analysis and …, 1998

Cited by 2869

Emergence of simple-cell receptive field properties by learning a sparse code for natural images

BA Olshausen – Nature, 1996

Cited by 3832

Shape matching and object recognition using shape contexts

S Belongie, J Malik, J Puzicha

Pattern Analysis and Machine Intelligence, IEEE Transactions on 24 (4), 509-522

Cited by 2547

The structure of images

JJ Koenderink – Biological cybernetics, 1984 – Springer

Cited by 2361

Shape and motion from image streams under orthography: a factorization method

Tomasi and Kanade – International Journal of Computer Vision, 1992

Cited by 2632

Active appearance models

TF Cootes, GJ Edwards… – Pattern Analysis and …, 2001

Cited by 2704

Scale & affine invariant interest point detectors

K Mikolajczyk, C Schmid

International journal of computer vision 60 (1), 63-86

Cited by 2025

Modeling and rendering architecture from photographs: A hybrid geometry-and image-based approach

PE Debevec, CJ Taylor, J Malik

Proceedings of the 23rd annual conference on Computer graphics and …

Cited by 1978

Feature extraction from faces using deformable templates

AL Yuille, PW Hallinan… – International journal of computer …, 1992

Cited by 2048

Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation

SC Zhu, A Yuille

Pattern Analysis and Machine Intelligence, IEEE Transactions on 18 (9), 884-900

Cited by 2948

Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories

S Lazebnik, C Schmid, J Ponce

Computer Vision and Pattern Recognition, 2006 IEEE Computer Society …

Cited by 2206

Face detection in color images

RL Hsu, M Abdel-Mottaleb, AK Jain – IEEE transactions on pattern …, 2002

Cited by 2148

Efficient graph-based image segmentation

PF Felzenszwalb… – International Journal of Computer …, 2004

Cited by 2112

Visual categorization with bags of keypoints

G Csurka, C Dance, L Fan, J Willamowski, C Bray – Workshop on statistical …, 2004

 

Cited by 1868

Object class recognition by unsupervised scale-invariant learning

R Fergus, P Perona, A Zisserman

Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE …

Cited by 1945

Recovering high dynamic range radiance maps from photographs

PE Debevec, J Malik

ACM SIGGRAPH 2008 classes, 31

Cited by 1896

A comparison of affine region detectors

K Mikolajczyk, T Tuytelaars, C Schmid, A Zisserman, J Matas, F Schaffalitzky …

International journal of computer vision 65 (1), 43-72

Cited by 1880

A bayesian hierarchical model for learning natural scene categories

L Fei-Fei… – Computer Vision and Pattern …, 2005

Note that the papers I listed here are just the ones that came up to my mind, let me know if I missed any important publications; I would be happy to make the list more complete. Also check out the website I made for organizing papers visually.

  1. “Fast approximate energy minimization via graph cuts” has over 2k citations.

  2. [...] by my M.S adviser Alan Yuille. In addition, the paper has 1651 citations and is listed in my most cited computer vision paper list. Apparently you need to publish high quality papers to get into the cartoon industry; this [...]

  3. B. D. Lucas and T. Kanade (1981), An iterative image registration technique with an application to stereo vision. Proceedings of Imaging Understanding Workshop, pages 121–130

    B.K.P. Horn and B.G. Schunck, “Determining optical flow.” Artificial Intelligence, vol 17, pp 185-203, 1981

  4. Eigenfaces:

    Turk and Pentland, Eigenfaces for Recognition, Vol. 3, No. 1, Pages 71-86, 1991 (9358 citations)

    Turk and Pentland, Face recognition using eigenfaces, CVPR, 1991 (3206 citations)

  5. My guess is that the Lucas-Kanade and Schunck-Horn papers have more citations but are vastly underestimated because they were published in vision’s “dark ages” (before 1988, indexing of IEEE vision papers begin in 1987). Although they appeared 1981, they still remain standard references to this day.

    • Right, without the “dark ages” the list would be far much longer. What I intended to do at first was to make a list about “the most cited object recognition papers” but I ended up using “the most cited papers in computer vision” since it sounds cooler and more searchable. I spent some time working on a website to organize papers, but stop working on it after I failed to get into PhD programs.

  6. Correction: indexing of IEEE vision papers begin in 1988

  7. Burt and Adelson, The Laplacian pyramid as a compact image code, IEEE Transactions on Communications, 1983. (4004 citations)

    Mallat, A theory for multiresolution signal decomposition: The wavelet representation, PAMI, 1989 (14455 citations)

    Isard and Blake, Condensation—conditional density propagation for visual tracking, IJCV, 1998 (3897 citations)

    Shi and Tomasi, Good Features to Track, CVPR 1994 (3746 citations)

    Tomasi and Kanade,Shape and motion from image streams under orthography: a factorization method, IJCV, 1992 (2147 citations)

    Geman and Geman, Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images, PAMI, 1984 (12841 citations)

    J. Canny, A computational approach to edge detection, PAMI, 1986 (13235 citations)

    JJ Koenderink, The structure of images, Biological cybernetics, 1984 (2151 citations)

  8. It’s worth remembering that the majority of these papers are highly cited, not because they report definitive solutions to problems, but because the papers launched discourses about important problems, hence the high citations. For example Horn’s optical flow sucks for motion analysis. I mean — really — you’re not going to use optical flow on real world video streams, are you, what with changing illumination, etc?

    • Right, many papers are highly cited because of opening a new field to research on. But that is equally valuable to a paper that has a break through solution.

      • I agree the papers have value. But I think today you can completely ignore some of them, like Turk/Pentland’s Eigenfaces crap. Viola/Jones and its descendants solve the problem very well, and the Eigenfaces nonsense can be happily forgotten. It’s not as though they invented PCA or anything!

      • But I also wanted to add that KGD has some excellent choices, and that, given your interest, you clearly belong in a PhD program. Where did you apply? There are lots of good departments not in the USN&WR Top Ten that wouldn’t be career-limiting.

  9. I am beginning a literature survey of computer vision documents. Do have a page of beginning or a primer articles for computer vision?
    Where do I start?
    Thanks
    Geri

    • If you have a specific topic to work on, you should be able to find some survey paper on google scholar, otherwise Wikipedia is a good start.

  10. Hi.
    May I suggest the paper entitled “Visual categorization with bags of keypoints” by G Csurka, C Dance, L Fan, J Willamowski and C Bray which appeared in the ECCV 2004 workshop on statistical learning in computer vision? it has 2,112 citations according to Google scholar.
    Cheers,
    Florent

  11. Mean shift: A robust approach toward feature space analysis

    Very interesting paper..for low level vision problems

  12. A model of -based visual attention for rapid scene analysis

    over 4k

  13. […] social science, organizational engineering, wearable computing (Google Glass), image understanding, and modern biometrics. His research has been featured in Nature, Science, and Harvard Business Review, as well as being […]

  14. […] of Oriented Gradient (HOG) are an extension of these edge detectors. An example of HOG is the quite successful paper on pedestrian detection “Histograms of oriented gradients for human detection” (See […]

  15. Here are some of advisor’s papers:

    Fingerprint image enhancement: algorithm and performance evaluation
    L Hong, Y Wan, A Jain
    Pattern Analysis and Machine Intelligence, IEEE Transactions on 20 (8), 777-789
    1998 (2046 citations)

    Statistical pattern recognition: A review
    AK Jain, RPW Duin, J Mao
    Pattern Analysis and Machine Intelligence, IEEE Transactions on 22 (1), 4-37
    2000 (4995 citations)
    Face detection in color images
    RL Hsu, M Abdel-Mottaleb, AK Jain
    Pattern Analysis and Machine Intelligence, IEEE Transactions on 24 (5), 696-706
    2002 (2206 citations)

    • Thanks, I added the face detection paper. I kind of hesitated on adding the other two papers though; the boundary of computer vision is not well defined and the other two papers kind of lie on the edge of what I view as computer vision research.

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