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UIUC同学Jia-Bin Huang收集的计算机视觉代码合集

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转自：http://blog.sina.com.cn/s/blog_631a4cc40100wrvz.html
UIUC的Jia-Bin Huang同学收集了很多计算机视觉方面的代码，链接如下：
https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html
这些代码很实用，可以让我们站在巨人的肩膀上~~

Topic

Resources

References

Feature Extraction

  • SIFT [1] [Demo program][SIFT Library] [VLFeat]

  • PCA-SIFT [2] [Project]

  • Affine-SIFT [3] [Project]

  • SURF [4] [OpenSURF] [Matlab Wrapper]

  • Affine Covariant Features [5] [Oxford project]

  • MSER [6] [Oxford project] [VLFeat]

  • Geometric Blur [7] [Code]

  • Local Self-Similarity Descriptor [8] [Oxford implementation]

  • Global and Efficient Self-Similarity [9] [Code]

  • Histogram of Oriented Graidents [10] [INRIA Object Localization Toolkit] [OLT toolkit for Windows]

  • GIST [11] [Project]

  • Shape Context [12] [Project]

  • Color Descriptor [13] [Project]

  • Pyramids of Histograms of Oriented Gradients [Code]

  • Space-Time Interest Points (STIP) [14] [Code]

  • Boundary Preserving Dense Local Regions [15][Project]

  1. D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints , IJCV 2004. [PDF]
  2. Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors ,CVPR , 2004. [PDF]
  3. J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison . SIAM Journal on Imaging Sciences , 2009. [PDF]
  4. H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features , ECCV , 2006. [PDF]
  5. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir and L. Van Gool, A comparison of affine region detectors . IJCV , 2005. [PDF]
  6. J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions . BMVC , 2002. [PDF]
  7. A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR , 2005. [PDF]
  8. E. Shechtman and M. Irani . Matching local self-similarities across images and videos, CVPR , 2007. [PDF]
  9. T. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection . CVPR 2010. [PDF]
  10. N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection . CVPR 2005. [PDF]
  11. A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope , IJCV , 2001. [PDF]
  12. S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts , PAMI , 2002. [PDF]
  13. K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene Recognition , PAMI , 2010.
  14. I. Laptev, On Space-Time Interest Points , IJCV, 2005. [PDF]
  15. J. Kim and K. Grauman, Boundary Preserving Dense Local Regions , CVPR 2011. [PDF]

Image Segmentation

  • Normalized Cut [1] [Matlab code]

  • Gerg Mori' Superpixel code [2] [Matlab code]

  • Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]

  • Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper]

  • OWT-UCM Hierarchical Segmentation [5] [Resources]

  • Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code]

  • Quick-Shift [7] [VLFeat]

  • SLIC Superpixels [8] [Project]

  • Segmentation by Minimum Code Length [9] [Project]

  • Biased Normalized Cut [10] [Project]

  • Segmentation Tree [11-12] [Project]

  • Entropy Rate Superpixel Segmentation [13] [Code]

  1. J. Shi and J Malik, Normalized Cuts and Image Segmentation , PAMI , 2000 [PDF]
  2. X. Ren and J. Malik. Learning a classification model for segmentation . ICCV , 2003. [PDF]
  3. P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation , IJCV 2004. [PDF]
  4. D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis . PAMI 2002. [PDF]
  5. P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation . PAMI , 2011. [PDF]
  6. A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric Flows , PAMI 2009. [PDF]
  7. A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking , ECCV , 2008. [PDF]
  8. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report , 2010. [PDF]
  9. A. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression , CVIU , 2007. [PDF]
  10. S. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut , CVPR 2011
  11. E. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,” ACCV 2009. [PDF]
  12. N. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996 [PDF]
  13. M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa , Entropy Rate Superpixel Segmentation, CVPR 2011 [PDF]

Object Detection

  • A simple object detector with boosting [Project]

  • INRIA Object Detection and Localization Toolkit [1] [Project]

  • Discriminatively Trained Deformable Part Models [2] [Project]

  • Cascade Object Detection with Deformable Part Models [3] [Project]

  • Poselet [4] [Project]

  • Implicit Shape Model [5] [Project]

  • Viola and Jones's Face Detection [6] [Project]
  1. N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection . CVPR 2005. [PDF]
  2. P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan.
    Object Detection with Discriminatively Trained Part Based Models , PAMI , 2010 [PDF]
  3. P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models . CVPR 2010 [PDF]
  4. L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations , ICCV 2009 [PDF]
  5. B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation , IJCV , 2008. [PDF]
  6. P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features , CVPR 2001. [PDF]

Saliency Detection

  • Itti, Koch, and Niebur' saliency detection [1] [Matlab code]

  • Frequency-tuned salient region detection [2] [Project]

  • Saliency detection using maximum symmetric surround [3] [Project]

  • Attention via Information Maximization [4] [Matlab code]

  • Context-aware saliency detection [5] [Matlab code]

  • Graph-based visual saliency [6] [Matlab code]

  • Saliency detection: A spectral residual approach. [7] [Matlab code]

  • Segmenting salient objects from images and videos. [8] [Matlab code]

  • Saliency Using Natural statistics. [9] [Matlab code]

  • Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] [Code]

  • Learning to Predict Where Humans Look [11] [Project]

  • Global Contrast based Salient Region Detection [12] [Project]
  1. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis . PAMI , 1998. [PDF]
  2. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection . In CVPR , 2009. [PDF]
  3. R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround . In ICIP , 2010. [PDF]
  4. N. Bruce and J. Tsotsos. Saliency based on information maximization . In NIPS , 2005. [PDF]
  5. S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection . In CVPR , 2010. [PDF]
  6. J. Harel, C. Koch, and P. Perona. Graph-based visual saliency . NIPS, 2007. [PDF]
  7. X. Hou and L. Zhang. Saliency detection: A spectral residual approach . CVPR , 2007. [PDF]
  8. E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos . CVPR , 2010. [PDF]
  9. L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statistics . Journal of Vision , 2008. [PDF]
  10. D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes , NIPS , 2004. [PDF]
  11. T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look , ICCV , 2009. [PDF]
  12. M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection . CVPR 2011.

Image Classification

  • Pyramid Match [1] [Project]

  • Spatial Pyramid Matching [2] [Code]

  • Locality-constrained Linear Coding [3] [Project] [Matlab code]

  • Sparse Coding [4] [Project] [Matlab code]

  • Texture Classification [5] [Project]

  • Multiple Kernels for Image Classification [6] [Project]

  • Feature Combination [7] [Project]

  • SuperParsing [Code]
  1. K. Grauman and T. Darrell, The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features , ICCV 2005. [PDF]
  2. S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , CVPR 2006 [PDF]
  3. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification , CVPR , 2010 [PDF]
  4. J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification , CVPR , 2009 [PDF]
  5. M. Varma and A. Zisserman, A statistical approach to texture classification from single images , IJCV2005. [PDF]
  6. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection . ICCV , 2009. [PDF]
  7. P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009. [PDF]
  8. J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image
    Parsing with Superpixels     , ECCV 2010. [PDF]

Category-Independent Object Proposal

  • Objectness measure [1] [Code]

  • Parametric min-cut [2] [Project]

  • Object proposal [3] [Project]

  1. B. Alexe, T. Deselaers, V. Ferrari, What is an Object? , CVPR 2010 [PDF]
  2. J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation , CVPR 2010. [PDF]
  3. I. Endres and D. Hoiem. Category Independent Object Proposals , ECCV 2010. [PDF]

MRF

  • Graph Cut [Project] [C++/Matlab Wrapper Code]
  1. Y. Boykov, O. Veksler and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001 [PDF]

Shadow Detection

  • Shadow Detection using Paired Region [Project]

  • Ground shadow detection [Project]

  1. R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions , CVPR 2011 [PDF]
  2. J.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs , ECCV 2010 [PDF]

Optical Flow

  • Kanade-Lucas-Tomasi Feature Tracker [C Code]

  • Optical Flow Matlab/C++ code by Ce Liu [Project]

  • Horn and Schunck's method by Deqing Sun [Code]

  • Black and Anandan's method by Deqing Sun [Code]

  • Optical flow code by Deqing Sun [Matlab Code] [Project]

  • Large Displacement Optical Flow by Thomas Brox [Executable for 64-bit Linux] [ Matlab Mex-functions for 64-bit Linux and 32-bit Windows] [Project]

  • Variational Optical Flow by Thomas Brox [Executable for 64-bit Linux] [ Executable for 32-bit Windows ] [ Matlab Mex-functions for 64-bit Linux and 32-bit Windows ] [Project]

  1. B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision , IJCAI 1981. [PDF]
  2. J. Shi, C. Tomasi, Good Feature to Track , CVPR 1994. [PDF]
  3. C. Liu. Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. Doctoral Thesis . MIT 2009. [PDF]
  4. B.K.P. Horn and B.G. Schunck, Determining Optical Flow , Artificial Intelligence 1981. [PDF]
  5. M. J. Black and P. Anandan, A framework for the robust estimation of optical flow, ICCV 93. [PDF]
  6. D. Sun, S. Roth, and M. J. Black, Secrets of optical flow estimation and their principles , CVPR 2010. [PDF]
  7. T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation , PAMI , 2010 [PDF]
  8. T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping , ECCV 2004 [PDF]

Object Tracking

  • Particle filter object tracking [1] [Project]

  • KLT Tracker [2-3] [Project]

  • MILTrack [4] [Code]

  • Incremental Learning for Robust Visual Tracking [5] [Project]

  • Online Boosting Trackers [6-7] [Project]

  • L1 Tracking [8] [Matlab code]

  1. P. Perez, C. Hue, J. Vermaak, and M. Gangnet. Color-Based Probabilistic Tracking ECCV , 2002. [PDF]
  2. B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision , IJCAI 1981. [PDF]
  3. J. Shi, C. Tomasi, Good Feature to Track , CVPR 1994. [PDF]
  4. B. Babenko, M. H. Yang, S. Belongie, Robust Object Tracking with Online Multiple Instance Learning , PAMI 2011 [PDF]
  5. D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking , IJCV 2007 [PDF]
  6. H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR 2006 [PDF]
  7. H. Grabner, C. Leistner, and H. Bischof, Semi-supervised On-line Boosting for Robust Tracking , ECCV 2008 [PDF]
  8. X. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV , 2009. [PDF]

Image Matting

  • Closed Form Matting [Code]

  • Spectral Matting [Project]

  • Learning-based Matting [Code]

  1. A. Levin D. Lischinski and Y. Weiss . A Closed Form Solution to Natural Image Matting , PAMI 2008 [PDF]
  2. A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting . PAMI 2008. [PDF]
  3. Y. Zheng and C. Kambhamettu, Learning Based Digital Matting , ICCV 2009 [PDF]

Bilateral Filtering

  • Fast Bilateral Filter [Project]

  • Real-time O(1) Bilateral Filtering [Code]

  • SVM for Edge-Preserving Filtering [Code]

  1. Q. Yang, K.-H. Tan and N. Ahuja, Real-time O(1) Bilateral Filtering ,
    CVPR 2009. [PDF]
  2. Q. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering ,
    CVPR 2010. [PDF]

Image Denoising

  • K-SVD [Matlab code]

  • BLS-GSM [Project]

  • BM3D [Project]

  • FoE [Code]

  • GFoE [Code]

  • Non-local means [Code]

  • Kernel regression [Code]

Image Super-Resolution

  • MRF for image super-resolution [Project]

  • Multi-frame image super-resolution [Project]

  • UCSC Super-resolution [Project]

  • Sprarse coding super-resolution [Code]

Image Deblurring

  • Eficient Marginal Likelihood Optimization in Blind Deconvolution [Code]

  • Analyzing spatially varying blur [Project]

  • Radon Transform [Code]

Image Quality Assessment

  • FSIM [1] [Project]

  • Degradation Model [2] [Project]

  • SSIM [3] [Project]

  • SPIQA [Code]

  1. L. Zhang, L. Zhang, X. Mou and D. Zhang, FSIM: A Feature Similarity Index for Image Quality Assessment , TIP 2011. [PDF]
  2. N. Damera-Venkata, and T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik , Image Quality Assessment Based on a Degradation Model , TIP 2000. [PDF]
  3. Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity, TIP 2004. [PDF]
  4. B. Ghanem, E. Resendiz, and N. Ahuja, Segmentation-Based Perceptual Image Quality Assessment (SPIQA) , ICIP 2008. [PDF]

Density Estimation

  • Kernel Density Estimation Toolbox [Project]

Dimension Reduction

  • Dimensionality Reduction Toolbox [Project]

  • ISOMAP [Code]

  • LLE [Project]

  • Laplacian Eigenmaps [Code]

  • Diffusion maps [Code]

Sparse Coding

Low-Rank Matrix Completion

Nearest Neighbors matching

  • ANN: Approximate Nearest Neighbor Searching [Project] [Matlab wrapper]

  • FLANN: Fast Library for Approximate Nearest Neighbors [Project]

Steoreo

  • StereoMatcher [Project]
  1. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms , IJCV 2002 [PDF]

Structure from motion

  • Boundler [1] [Project]
  1. N. Snavely, S. M. Seitz, R. Szeliski. Photo Tourism: Exploring image collections in 3D . SIGGRAPH , 2006. [PDF]

Distance Transformation

  • Distance Transforms of Sampled Functions [1] [Project]
  1. P. F. Felzenszwalb and D. P. Huttenlocher. Distance transforms of sampled functions . Technical report, Cornell University , 2004. [PDF]

Chamfer Matching

  • Fast Directional Chamfer Matching [Code]
  1. M.-Y. Liu, O. Tuzel, A. Veeraraghavan, and R. Chellappa, Fast Directional Chamfer Matching , CVPR 2010 [PDF]

Clustering

  • K-Means [VLFeat] [Oxford code]

  • Spectral Clustering [UW Project][Code] [Self-Tuning code]

  • Affinity Propagation [Project]

Classification

  • SVM [Libsvm] [SVM-Light] [SVM-Struct]

  • Boosting

  • Naive Bayes

Regression

  • SVM

  • RVM

  • GPR

Multiple Kernel Learning (MKL)

  • SHOGUN [Project]

  • OpenKernel.org [Project]

  • DOGMA (online algorithms) [Project]

  • SimpleMKL [Project]

  1. S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning . JMLR , 2006. [PDF]
  2. F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML , 2011. [PDF]
  3. F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning . CVPR , 2010. [PDF]
  4. A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl . JMRL , 2008. [PDF]

Multiple Instance Learning (MIL)

  • MIForests [1] [Project]

  • MILIS [2]

  • MILES [3] [Project] [Code]

  • DD-SVM [4] [Project]

  1. C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees , ECCV 2010. [PDF]
  2. Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection , PAMI 2010. [PDF]
  3. Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection . PAMI 2006 [PDF]
  4. Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions , JMLR 2004. [PDF]

Other Utilities

  • Code for downloading Flickr images, by James Hays [Code]

  • The Lightspeed Matlab Toolbox by Tom Minka [Code]

  • MATLAB Functions for Multiple View Geometry [Code]

  • Peter's Functions for Computer Vision [Code]

  • Statistical Pattern Recognition Toolbox [Code]

Useful Links (dataset, lectures, and other softwares)

Conference Information

  • Computer Image Analysis, Computer Vision Conferences

Papers

  • Computer vision paper on the web

  • NIPS Proceedings

Datasets

  • Compiled list of recognition datasets

  • Computer vision dataset from CMU

Lectures

  • Videolectures

Source Codes

  • Computer Vision Algorithm Implementations

  • OpenCV

  • Source Code Collection for Reproducible Research

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