[CODE]UIUC同学Jia-Bin Huang收集的计算机视觉代码合集

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
http://blog.sciencenet.cn/blog-722391-569547.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. InICIP, 2010. [PDF] 4. N. Bruce and J. Tsotsos. Saliency based on information maximization. InNIPS, 2005. [PDF] 5. S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. InCVPR, 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 [b]Parsing with Superpixels[/b], 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 TrackingECCV, 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]

UsefulLinks (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