计算机视觉方面的代码

原文：http://blog.youkuaiyun.com/xinanzhung/article/details/8790875

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. GoolSURF: 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   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  Intelligence1981. [PDF] 5.      M. J. Black and P. Anandan, A framework for the robust estimation of  optical flow,ICCV93. [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