UIUC同学Jia-Bin Huang收集的计算机视觉代码合集（ZZ）

转自：http://www.cnblogs.com/idaidai/archive/2012/03/01/2375800.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

248 item Topic Name Reference Link Feature Detection,Feature Extraction, and Action Recognition Space-Time Interest Points (STIP) I. Laptev, On Space-Time Interest Points, IJCV, 2005 and I. Laptev and T. Lindeberg, On Space-Time Interest Points, IJCV 2005 http://www.irisa.fr/vista/Equipe/People/Laptev/download/stip-1.1-winlinux.zip andhttp://www.nada.kth.se/cvap/abstracts/cvap284.html Action Recognition 3D Gradients (HOG3D) A. Klaser, M. Marszałek, and C. Schmid, BMVC, 2008. http://lear.inrialpes.fr/people/klaeser/research_hog3d Action Recognition Dense Trajectories Video Description H. Wang and A. Klaser and C. Schmid and C.- L. Liu, Action Recognition by Dense Trajectories, CVPR, 2011 http://lear.inrialpes.fr/people/wang/dense_trajectories Alpha Matting Spectral Matting A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008 http://www.vision.huji.ac.il/SpectralMatting/ Alpha Matting Shared Matting E. S. L. Gastal and M. M. Oliveira, Computer Graphics Forum, 2010 http://www.inf.ufrgs.br/~eslgastal/SharedMatting/ Alpha Matting Bayesian Matting Y. Y. Chuang, B. Curless, D. H. Salesin, and R. Szeliski, A Bayesian Approach to Digital Matting, CVPR, 2001 http://www1.idc.ac.il/toky/CompPhoto-09/Projects/Stud_projects/Miki/index.html Alpha Matting Closed Form Matting A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting, PAMI 2008. http://people.csail.mit.edu/alevin/matting.tar.gz Alpha Matting Learning-based Matting Y. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009 http://www.mathworks.com/matlabcentral/fileexchange/31412 Camera Calibration Camera Calibration Toolbox for Matlab http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/ref.html http://www.vision.caltech.edu/bouguetj/calib_doc/ Camera Calibration EasyCamCalib J. Barreto, J. Roquette, P. Sturm, and F. Fonseca, Automatic camera calibration applied to medical endoscopy, BMVC, 2009 http://arthronav.isr.uc.pt/easycamcalib/ Camera Calibration Epipolar Geometry Toolbox G.L. Mariottini, D. Prattichizzo, EGT: a Toolbox for Multiple View Geometry and Visual Servoing, IEEE Robotics & Automation Magazine, 2005 http://egt.dii.unisi.it/ Clustering Spectral Clustering - UW Project   http://www.stat.washington.edu/spectral/ Clustering Spectral Clustering - UCSD Project   http://vision.ucsd.edu/~sagarwal/spectral-0.2.tgz Clustering Self-Tuning Spectral Clustering   http://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.html Clustering K-Means - Oxford Code   http://www.cs.ucf.edu/~vision/Code/vggkmeans.zip Clustering K-Means - VLFeat   http://www.vlfeat.org/ Common Visual Pattern Discovery Sketching the Common S. Bagon, O. Brostovsky, M. Galun and M. Irani, Detecting and Sketching the Common, CVPR 2010 http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/SketchCommonCVPR10_v1.1.tar.gz Common Visual Pattern Discovery Common Visual Pattern Discovery via Spatially Coherent Correspondences H. Liu, S. Yan, "Common Visual Pattern Discovery via Spatially Coherent Correspondences", CVPR 2010 https://sites.google.com/site/lhrbss/home/papers/SimplifiedCode.zip?attredirects=0 Density Estimation Kernel Density Estimation Toolbox   http://www.ics.uci.edu/~ihler/code/kde.html Depth Sensor Kinect SDK http://www.microsoft.com/en-us/kinectforwindows/ http://www.microsoft.com/en-us/kinectforwindows/ Dimension Reduction ISOMAP   http://isomap.stanford.edu/ Dimension Reduction LLE   http://www.cs.nyu.edu/~roweis/lle/code.html Dimension Reduction Laplacian Eigenmaps   http://www.cse.ohio-state.edu/~mbelkin/algorithms/Laplacian.tar Dimension Reduction Diffusion maps   http://www.stat.cmu.edu/~annlee/software.htm Dimension Reduction Dimensionality Reduction Toolbox   http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html Distance Metric Learning Matlab Toolkit for Distance Metric Learning   http://www.cs.cmu.edu/~liuy/distlearn.htm Distance Transformation Distance Transforms of Sampled Functions   http://people.cs.uchicago.edu/~pff/dt/ Feature Detection Canny Edge Detection J. Canny, A Computational Approach To Edge Detection, PAMI, 1986 http://www.mathworks.com/help/toolbox/images/ref/edge.html Feature Detection FAST Corner Detection E. Rosten and T. Drummond, Machine learning for high-speed corner detection, ECCV, 2006 http://www.edwardrosten.com/work/fast.html Feature Detection Edge Foci Interest Points L. Zitnickand K. Ramnath, Edge Foci Interest Points, ICCV, 2011 http://research.microsoft.com/en-us/um/people/larryz/edgefoci/edge_foci.htm Feature Detection Boundary Preserving Dense Local Regions J. Kim and K. Grauman, Boundary Preserving Dense Local Regions, CVPR 2011 http://vision.cs.utexas.edu/projects/bplr/bplr.html Feature Extraction BRIEF: Binary Robust Independent Elementary Features M. Calonder, V. Lepetit, C. Strecha, P. Fua, BRIEF: Binary Robust Independent Elementary Features, ECCV 2010 http://cvlab.epfl.ch/research/detect/brief/ Feature Detection andFeature Extraction Scale-invariant feature transform (SIFT) - VLFeat D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. http://www.vlfeat.org/ Feature Detection andFeature Extraction Scale-invariant feature transform (SIFT) - Demo Software D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. http://www.cs.ubc.ca/~lowe/keypoints/ Feature Extraction Global and Efficient Self-Similarity T. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection. CVPR 2010and T. Deselaers, V. Ferrari, Global and Efficient Self-Similarity for Object Classification and Detection, CVPR 2010 http://www.vision.ee.ethz.ch/~calvin/gss/selfsim_release1.0.tgz Feature Detection andFeature Extraction Affine-SIFT J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009 http://www.ipol.im/pub/algo/my_affine_sift/ Feature Detection andFeature Extraction Geometric Blur A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR, 2005 http://www.robots.ox.ac.uk/~vgg/software/MKL/ Feature Extraction PCA-SIFT Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004 http://www.cs.cmu.edu/~yke/pcasift/ Feature Detection andFeature Extraction Scale-invariant feature transform (SIFT) - Library D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. http://blogs.oregonstate.edu/hess/code/sift/ Feature Detection andFeature Extraction Groups of Adjacent Contour Segments V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, Groups of Adjacent Contour Segments for Object Detection, PAMI, 2007 http://www.robots.ox.ac.uk/~vgg/share/ferrari/release-kas-v102.tgz Feature Detection andFeature Extraction Speeded Up Robust Feature (SURF) - Matlab Wrapper H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006 http://www.maths.lth.se/matematiklth/personal/petter/surfmex.php Feature Extraction Shape Context S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002 http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sc_digits.html Feature Detection andFeature Extraction Speeded Up Robust Feature (SURF) - Open SURF H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006 http://www.chrisevansdev.com/computer-vision-opensurf.html Feature Detection andFeature Extraction Maximally stable extremal regions (MSER) J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002 http://www.robots.ox.ac.uk/~vgg/research/affine/ Feature Extraction GIST Descriptor A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, IJCV, 2001 http://people.csail.mit.edu/torralba/code/spatialenvelope/ Feature Detection andFeature Extraction Color Descriptor K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene Recognition, PAMI, 2010 http://koen.me/research/colordescriptors/ Feature Extraction Local Self-Similarity Descriptor E. Shechtman and M. Irani. Matching local self-similarities across images and videos, CVPR, 2007 http://www.robots.ox.ac.uk/~vgg/software/SelfSimilarity/ Feature Detection andFeature Extraction Maximally stable extremal regions (MSER) - VLFeat J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002 http://www.vlfeat.org/ Feature Extraction Pyramids of Histograms of Oriented Gradients (PHOG) A. Bosch, A. Zisserman, and X. Munoz, Representing shape with a spatial pyramid kernel, CIVR, 2007 http://www.robots.ox.ac.uk/~vgg/research/caltech/phog/phog.zip Feature Detection andFeature Extraction Affine Covariant Features T. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors: A Survey, Foundations and Trends in Computer Graphics and Vision, 2008 http://www.robots.ox.ac.uk/~vgg/research/affine/ Feature Extraction sRD-SIFT M. Lourenco, J. P. Barreto and A. Malti, Feature Detection and Matching in Images with Radial Distortion, ICRA 2010 http://arthronav.isr.uc.pt/~mlourenco/srdsift/index.html# Graph Matching Reweighted Random Walks for Graph Matching M. Cho, J. Lee, and K. M. Lee, Reweighted Random Walks for Graph Matching, ECCV 2010 http://cv.snu.ac.kr/research/~RRWM/ Graph Matching Hyper-graph Matching via Reweighted Random Walks J. Lee, M. Cho, K. M. Lee. "Hyper-graph Matching via Reweighted Random Walks", CVPR 2011 http://cv.snu.ac.kr/research/~RRWHM/ Illumination, Reflectance, and Shadow Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse Sequences J-F. Lalonde, A. A. Efros, S. G. Narasimhan, Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse Sequences, SIGGRAPH Asia 2009 http://www.cs.cmu.edu/~jlalonde/software.html#skyModel Illumination, Reflectance, and Shadow Ground shadow detection J.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs, ECCV 2010 http://www.jflalonde.org/software.html#shadowDetection Illumination, Reflectance, and Shadow Shadow Detection using Paired Region R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011 http://www.cs.illinois.edu/homes/guo29/projects/shadow.html Illumination, Reflectance, and Shadow Real-time Specular Highlight Removal Q. Yang, S. Wang and N. Ahuja, Real-time Specular Highlight Removal Using Bilateral Filtering, ECCV 2010 http://www.cs.cityu.edu.hk/~qiyang/publications/code/eccv-10.zip Illumination, Reflectance, and Shadow Estimating Natural Illumination from a Single Outdoor Image J-F. Lalonde, A. A. Efros, S. G. Narasimhan, Estimating Natural Illumination from a Single Outdoor Image , ICCV 2009 http://www.cs.cmu.edu/~jlalonde/software.html#skyModel Illumination, Reflectance, and Shadow What Does the Sky Tell Us About the Camera? J-F. Lalonde, S. G. Narasimhan, A. A. Efros, What Does the Sky Tell Us About the Camera?, ECCV 2008 http://www.cs.cmu.edu/~jlalonde/software.html#skyModel Image Classification Locality-constrained Linear Coding J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification, CVPR, 2010 http://www.ifp.illinois.edu/~jyang29/LLC.htm Image Classification Sparse Coding for Image Classification J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification, CVPR, 2009 http://www.ifp.illinois.edu/~jyang29/ScSPM.htm Image Classification Texture Classification M. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005 http://www.robots.ox.ac.uk/~vgg/research/texclass/index.html Feature Matching andImage Classification The Pyramid Match: Efficient Matching for Retrieval and Recognition K. Grauman and T. Darrell. The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005 http://www.cs.utexas.edu/~grauman/research/projects/pmk/pmk_projectpage.htm Image Classification Spatial Pyramid Matching S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, CVPR 2006 http://www.cs.unc.edu/~lazebnik/research/SpatialPyramid.zip Image Deblurring Radon Transform T. S. Cho, S. Paris, B. K. P. Horn, W. T. Freeman, Blur kernel estimation using the radon transform, CVPR 2011 http://people.csail.mit.edu/taegsang/Documents/RadonDeblurringCode.zip Image Deblurring Analyzing spatially varying blur A. Chakrabarti, T. Zickler, and W. T. Freeman, Analyzing Spatially-varying Blur, CVPR 2010 http://www.eecs.harvard.edu/~ayanc/svblur/ Image Denoising,Image Super-resolution, and Image Deblurring Learning Models of Natural Image Patches D. Zoran and Y. Weiss, From Learning Models of Natural Image Patches to Whole Image Restoration, ICCV, 2011 http://www.cs.huji.ac.il/~daniez/ Image Deblurring Non-blind deblurring (and blind denoising) with integrated noise estimation U. Schmidt, K. Schelten, and S. Roth. Bayesian deblurring with integrated noise estimation, CVPR 2011 http://www.gris.tu-darmstadt.de/research/visinf/software/index.en.htm Image Deblurring Eficient Marginal Likelihood Optimization in Blind Deconvolution A. Levin, Y. Weiss, F. Durand, W. T. Freeman. Efficient Marginal Likelihood Optimization in Blind Deconvolution, CVPR 2011 http://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR2011Code.zip Image Deblurring Richardson-Lucy Deblurring for Scenes under Projective Motion Path Y.-W. Tai, P. Tan, M. S. Brown: Richardson-Lucy Deblurring for Scenes under Projective Motion Path, PAMI 2011 http://yuwing.kaist.ac.kr/projects/projectivedeblur/projectivedeblur_files/ProjectiveDeblur.zip Image Denoising Sparsity-based Image Denoising W. Dong, X. Li, L. Zhang and G. Shi, Sparsity-based Image Denoising vis Dictionary Learning and Structural Clustering, CVPR, 2011 http://www.csee.wvu.edu/~xinl/CSR.html Image Denoising K-SVD   http://www.cs.technion.ac.il/~ronrubin/Software/ksvdbox13.zip Image Denoising Clustering-based Denoising P. Chatterjee and P. Milanfar, Clustering-based Denoising with Locally Learned Dictionaries (K-LLD), TIP, 2009 http://users.soe.ucsc.edu/~priyam/K-LLD/ Image Denoising BLS-GSM   http://decsai.ugr.es/~javier/denoise/ Image Denoising Field of Experts   http://www.cs.brown.edu/~roth/research/software.html Image Denoising Non-local Means   http://dmi.uib.es/~abuades/codis/NLmeansfilter.m Image Denoising What makes a good model of natural images ? Y. Weiss and W. T. Freeman, CVPR 2007 http://www.cs.huji.ac.il/~yweiss/BRFOE.zip Image Denoising BM3D   http://www.cs.tut.fi/~foi/GCF-BM3D/ Image Denoising Kernel Regressions   http://www.soe.ucsc.edu/~htakeda/MatlabApp/KernelRegressionBasedImageProcessingToolBox_ver1-1beta.zip Image Denoising Gaussian Field of Experts   http://www.cs.huji.ac.il/~yweiss/BRFOE.zip Image Denoising Nonlocal means with cluster trees T. Brox, O. Kleinschmidt, D. Cremers, Efficient nonlocal means for denoising of textural patterns, TIP 2008 http://lmb.informatik.uni-freiburg.de/resources/binaries/nlmeans_brox_tip08Linux64.zip Image Filtering GradientShop P. Bhat, C.L. Zitnick, M. Cohen, B. Curless, and J. Kim, GradientShop: A Gradient-Domain Optimization Framework for Image and Video Filtering, TOG 2010 http://grail.cs.washington.edu/projects/gradientshop/ Image Filtering Weighted Least Squares Filter Z. Farbman, R. Fattal, D. Lischinski, R. Szeliski, Edge-Preserving Decompositions for Multi-Scale Tone and Detail Manipulation, SIGGRAPH 2008 http://www.cs.huji.ac.il/~danix/epd/ Image Filtering Real-time O(1) Bilateral Filtering Q. Yang, K.-H. Tan and N. Ahuja, Real-time O(1) Bilateral Filtering, CVPR 2009 http://vision.ai.uiuc.edu/~qyang6/publications/code/qx_constant_time_bilateral_filter_ss.zip Image Filtering Guided Image Filtering K. He, J. Sun, X. Tang, Guided Image Filtering, ECCV 2010 http://personal.ie.cuhk.edu.hk/~hkm007/eccv10/guided-filter-code-v1.rar Image Filtering Fast Bilateral Filter S. Paris and F. Durand, A Fast Approximation of the Bilateral Filter using a Signal Processing Approach, ECCV, 2006 http://people.csail.mit.edu/sparis/bf/ Image Filtering Image smoothing via L0 Gradient Minimization L. Xu, C. Lu, Y. Xu, J. Jia, Image smoothing via L0 Gradient Minimization, SIGGRAPH Asia 2011 http://www.cse.cuhk.edu.hk/~leojia/projects/L0smoothing/L0smoothing.zip Image Filtering Domain Transformation E. Gastal, M. Oliveira, Domain Transform for Edge-Aware Image and Video Processing, SIGGRAPH 2011 http://inf.ufrgs.br/~eslgastal/DomainTransform/DomainTransformFilters-Source-v1.0.zip Image Processing andImage Filtering Piotr's Image & Video Matlab Toolbox Piotr Dollar, Piotr's Image & Video Matlab Toolbox, http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html Image Filtering Local Laplacian Filters S. Paris, S. Hasinoff, J. Kautz, Local Laplacian Filters: Edge-Aware Image Processing with a Laplacian Pyramid, SIGGRAPH 2011 http://people.csail.mit.edu/sparis/publi/2011/siggraph/matlab_source_code.zip Image Filtering SVM for Edge-Preserving Filtering Q. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering, CVPR 2010 http://vision.ai.uiuc.edu/~qyang6/publications/code/cvpr-10-svmbf/program_video_conferencing.zip Image Filtering Anisotropic Diffusion P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, PAMI 1990 http://www.mathworks.com/matlabcentral/fileexchange/14995-anisotropic-diffusion-perona-malik Image Quality Assessment SPIQA   http://vision.ai.uiuc.edu/~bghanem2/shared_code/SPIQA_code.zip Image Quality Assessment Degradation Model   http://users.ece.utexas.edu/~bevans/papers/2000/imageQuality/index.html Image Quality Assessment Feature SIMilarity Index   http://www4.comp.polyu.edu.hk/~cslzhang/IQA/FSIM/FSIM.htm Image Quality Assessment Structural SIMilarity   https://ece.uwaterloo.ca/~z70wang/research/ssim/ Image Segmentation Segmentation by Minimum Code Length A. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression, CVIU, 2007 http://perception.csl.uiuc.edu/coding/image_segmentation/ Image Segmentation Normalized Cut J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000 http://www.cis.upenn.edu/~jshi/software/ Image Segmentation Entropy Rate Superpixel Segmentation M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011 http://www.umiacs.umd.edu/~mingyliu/src/ers_matlab_wrapper_v0.1.zip Image Segmentation Mean-Shift Image Segmentation - EDISON D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002 http://coewww.rutgers.edu/riul/research/code/EDISON/index.html Image Segmentation Efficient Graph-based Image Segmentation - Matlab Wrapper P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004 http://www.mathworks.com/matlabcentral/fileexchange/25866-efficient-graph-based-image-segmentation Image Segmentation Biased Normalized Cut S. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut, CVPR 2011 http://www.cs.berkeley.edu/~smaji/projects/biasedNcuts/ Image Segmentation Multiscale Segmentation Tree E. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,” ACCV 2009 and N. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996 http://vision.ai.uiuc.edu/segmentation Image Segmentation Efficient Graph-based Image Segmentation - C++ code P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004 http://people.cs.uchicago.edu/~pff/segment/ Image Segmentation Superpixel by Gerg Mori X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003 http://www.cs.sfu.ca/~mori/research/superpixels/ Image Segmentation Segmenting Scenes by Matching Image Composites B. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, NIPS 2009 http://www.cs.washington.edu/homes/bcr/projects/SceneComposites/index.html Image Segmentation Recovering Occlusion Boundaries from a Single Image D. Hoiem, A. Stein, A. A. Efros, M. Hebert, Recovering Occlusion Boundaries from a Single Image, ICCV 2007. http://www.cs.cmu.edu/~dhoiem/software/ Image Segmentation Quick-Shift A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking, ECCV, 2008 http://www.vlfeat.org/overview/quickshift.html Image Segmentation SLIC Superpixels R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010 http://ivrg.epfl.ch/supplementary_material/RK_SLICSuperpixels/index.html Image Segmentation Mean-Shift Image Segmentation - Matlab Wrapper D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002 http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/edison_matlab_interface.tar.gz Image Segmentation OWT-UCM Hierarchical Segmentation P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011 http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html Image Segmentation Turbepixels A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric Flows, PAMI 2009 http://www.cs.toronto.edu/~babalex/research.html Image Super-resolution MRF for image super-resolution W. T Freeman and C. Liu. Markov Random Fields for Super-resolution and Texture Synthesis. In A. Blake, P. Kohli, and C. Rother, eds., Advances in Markov Random Fields for Vision and Image Processing, Chapter 10. MIT Press, 2011 http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.html Image Super-resolution Single-Image Super-Resolution Matlab Package R. Zeyde, M. Elad, and M. Protter, On Single Image Scale-Up using Sparse-Representations, LNCS 2010 http://www.cs.technion.ac.il/~elad/Various/Single_Image_SR.zip Image Super-resolution Self-Similarities for Single Frame Super-Resolution C.-Y. Yang, J.-B. Huang, and M.-H. Yang, Exploiting Self-Similarities for Single Frame Super-Resolution, ACCV 2010 https://eng.ucmerced.edu/people/cyang35/ACCV10.zip Image Super-resolution MDSP Resolution Enhancement Software S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Fast and Robust Multi-frame Super-resolution, TIP 2004 http://users.soe.ucsc.edu/~milanfar/software/superresolution.html Image Super-resolution Sprarse coding super-resolution J. Yang, J. Wright, T. S. Huang, and Y. Ma. Image super-resolution via sparse representation, TIP 2010 http://www.ifp.illinois.edu/~jyang29/ScSR.htm Image Super-resolution Multi-frame image super-resolution Pickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis http://www.robots.ox.ac.uk/~vgg/software/SR/index.html Image Understanding SuperParsing J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image Parsing with Superpixels, ECCV 2010 http://www.cs.unc.edu/~jtighe/Papers/ECCV10/eccv10-jtighe-code.zip Image Understanding Discriminative Models for Multi-Class Object Layout C. Desai, D. Ramanan, C. Fowlkes. "Discriminative Models for Multi-Class Object Layout, IJCV 2011 http://www.ics.uci.edu/~desaic/multiobject_context.zip Image Understanding Nonparametric Scene Parsing via Label Transfer C. Liu, J. Yuen, and Antonio Torralba, Nonparametric Scene Parsing via Label Transfer, PAMI 2011 http://people.csail.mit.edu/celiu/LabelTransfer/index.html Image Understanding Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics A. Gupta, A. A. Efros, M. Hebert, Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics, ECCV 2010 http://www.cs.cmu.edu/~abhinavg/blocksworld/#downloads Image Understanding Towards Total Scene Understanding L.-J. Li, R. Socher and Li F.-F.. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework, CVPR 2009 http://vision.stanford.edu/projects/totalscene/index.html Image Understanding Object Bank Li-Jia Li, Hao Su, Eric P. Xing and Li Fei-Fei. Object Bank: A High-Level Image Representation for Scene Classification and Semantic Feature Sparsification, NIPS 2010 http://vision.stanford.edu/projects/objectbank/index.html Kernels and Distances Fast Directional Chamfer Matching   http://www.umiacs.umd.edu/~mingyliu/src/fdcm_matlab_wrapper_v0.2.zip Kernels and Distances Efficient Earth Mover's Distance with L1 Ground Distance (EMD_L1) H. Ling and K. Okada, An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison, PAMI 2007 http://www.dabi.temple.edu/~hbling/code/EmdL1_v3.zip Kernels and Distances Diffusion-based distance H. Ling and K. Okada, Diffusion Distance for Histogram Comparison, CVPR 2006 http://www.dabi.temple.edu/~hbling/code/DD_v1.zip Low-Rank Modeling TILT: Transform Invariant Low-rank Textures Z. Zhang, A. Ganesh, X. Liang, and Y. Ma, TILT: Transform Invariant Low-rank Textures, IJCV 2011 http://perception.csl.uiuc.edu/matrix-rank/tilt.html Low-Rank Modeling Low-Rank Matrix Recovery and Completion   http://perception.csl.uiuc.edu/matrix-rank/sample_code.html Low-Rank Modeling RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition Y. Peng, A. Ganesh, J. Wright, W. Xu, and Y. Ma, RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition, CVPR 2010 http://perception.csl.uiuc.edu/matrix-rank/rasl.html MRF Optimization MRF Minimization Evaluation R. Szeliski et al., A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, PAMI, 2008 http://vision.middlebury.edu/MRF/ MRF Optimization Max-flow/min-cut for shape fitting V. Lempitsky and Y. Boykov, Global Optimization for Shape Fitting, CVPR 2007 http://www.csd.uwo.ca/faculty/yuri/Implementations/TouchExpand.zip MRF Optimization Max-flow/min-cut Y. Boykov and V. Kolmogorov, An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, PAMI 2004 http://vision.csd.uwo.ca/code/maxflow-v3.01.zip MRF Optimization Planar Graph Cut F. R. Schmidt, E. Toppe and D. Cremers, Efficient Planar Graph Cuts with Applications in Computer Vision, CVPR 2009 http://vision.csd.uwo.ca/code/PlanarCut-v1.0.zip MRF Optimization Max-flow/min-cut for massive grids A. Delong and Y. Boykov, A Scalable Graph-Cut Algorithm for N-D Grids, CVPR 2008 http://vision.csd.uwo.ca/code/regionpushrelabel-v1.03.zip MRF Optimization Multi-label optimization Y. Boykov, O. Verksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001 http://vision.csd.uwo.ca/code/gco-v3.0.zip Machine Learning Statistical Pattern Recognition Toolbox M.I. Schlesinger, V. Hlavac: Ten lectures on the statistical and structural pattern recognition, Kluwer Academic Publishers, 2002 http://cmp.felk.cvut.cz/cmp/software/stprtool/ Machine Learning Netlab Neural Network Software C. M. Bishop, Neural Networks for Pattern RecognitionㄝOxford University Press, 1995 http://www1.aston.ac.uk/eas/research/groups/ncrg/resources/netlab/ Machine Learning Boosting Resources by Liangliang Cao http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm Machine Learning FastICA package for MATLAB http://research.ics.tkk.fi/ica/book/ http://research.ics.tkk.fi/ica/fastica/ Multi-View Stereo Patch-based Multi-view Stereo Software Y. Furukawa and J. Ponce, Accurate, Dense, and Robust Multi-View Stereopsis, PAMI 2009 http://grail.cs.washington.edu/software/pmvs/ Multi-View Stereo Clustering Views for Multi-view Stereo Y. Furukawa, B. Curless, S. M. Seitz, and R. Szeliski, Towards Internet-scale Multi-view Stereo, CVPR 2010 http://grail.cs.washington.edu/software/cmvs/ Multi-View Stereo Multi-View Stereo Evaluation S. Seitz et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006 http://vision.middlebury.edu/mview/ Multiple Instance Learning DD-SVM Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 2004   Multiple Instance Learning MIForests C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010 http://www.ymer.org/amir/software/milforests/ Multiple Instance Learning MILIS Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010   Multiple Instance Learning MILES Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection. PAMI 2006 http://infolab.stanford.edu/~wangz/project/imsearch/SVM/PAMI06/ Multiple Kernel Learning SHOGUN S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning. JMLR, 2006 http://www.shogun-toolbox.org/ Multiple Kernel Learning OpenKernel.org F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011 http://www.openkernel.org/ Multiple Kernel Learning SimpleMKL A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl. JMRL, 2008 http://asi.insa-rouen.fr/enseignants/~arakotom/code/mklindex.html Multiple Kernel Learning DOGMA F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010 http://dogma.sourceforge.net/ Multiple View Geometry MATLAB and Octave Functions for Computer Vision and Image Processing P. D. Kovesi. MATLAB and Octave Functions for Computer Vision and Image Processing, http://www.csse.uwa.edu.au/~pk/research/matlabfns http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.html Multiple View Geometry Matlab Functions for Multiple View Geometry   http://www.robots.ox.ac.uk/~vgg/hzbook/code/ Nearest Neighbors Matching ANN: Approximate Nearest Neighbor Searching   http://www.cs.umd.edu/~mount/ANN/ Nearest Neighbors Matching Spectral Hashing Y. Weiss, A. Torralba, R. Fergus, Spectral Hashing, NIPS 2008 http://www.cs.huji.ac.il/~yweiss/SpectralHashing/ Nearest Neighbors Matching Coherency Sensitive Hashing S. Korman, S. Avidan, Coherency Sensitive Hashing, ICCV 2011 http://www.eng.tau.ac.il/~simonk/CSH/index.html Nearest Neighbors Matching FLANN: Fast Library for Approximate Nearest Neighbors   http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN Nearest Neighbors Matching LDAHash: Binary Descriptors for Matching in Large Image Databases C. Strecha, A. M. Bronstein, M. M. Bronstein and P. Fua. LDAHash: Improved matching with smaller descriptors, PAMI, 2011. http://cvlab.epfl.ch/research/detect/ldahash/index.php Object Detection Poselet L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009 http://www.eecs.berkeley.edu/~lbourdev/poselets/ Object Detection Cascade Object Detection with Deformable Part Models P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models. CVPR, 2010 http://people.cs.uchicago.edu/~rbg/star-cascade/ Object Detection Multiple Kernels A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection. ICCV, 2009 http://www.robots.ox.ac.uk/~vgg/software/MKL/ Object Detection Hough Forests for Object Detection J. Gall and V. Lempitsky, Class-Specific Hough Forests for Object Detection, CVPR, 2009 http://www.vision.ee.ethz.ch/~gallju/projects/houghforest/index.html Object Detection Discriminatively Trained Deformable Part Models P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models, PAMI, 2010 http://people.cs.uchicago.edu/~pff/latent/ Feature Extraction andObject Detection Histogram of Oriented Graidents - OLT for windows N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005 http://www.computing.edu.au/~12482661/hog.html Feature Extraction andObject Detection Histogram of Oriented Graidents - INRIA Object Localization Toolkit N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005 http://www.navneetdalal.com/software Object Detection Recognition using regions C. Gu, J. J. Lim, P. Arbelaez, and J. Malik, CVPR 2009 http://www.cs.berkeley.edu/~chunhui/publications/cvpr09_v2.zip Object Detection A simple parts and structure object detector ICCV 2005 short courses on Recognizing and Learning Object Categories http://people.csail.mit.edu/fergus/iccv2005/partsstructure.html Object Detection Feature Combination P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009 http://www.vision.ee.ethz.ch/~pgehler/projects/iccv09/index.html Object Detection Ensemble of Exemplar-SVMs T. Malisiewicz, A. Gupta, A. Efros. Ensemble of Exemplar-SVMs for Object Detection and Beyond . ICCV, 2011 http://www.cs.cmu.edu/~tmalisie/projects/iccv11/ Object Detection A simple object detector with boosting ICCV 2005 short courses on Recognizing and Learning Object Categories http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html Object Detection Max-Margin Hough Transform S. Maji and J. Malik, Object Detection Using a Max-Margin Hough Transform. CVPR 2009 http://www.cs.berkeley.edu/~smaji/projects/max-margin-hough/ Object Detection Implicit Shape Model B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation, IJCV, 2008 http://www.vision.ee.ethz.ch/~bleibe/code/ism.html Object Detection Ensemble of Exemplar-SVMs for Object Detection and Beyond T. Malisiewicz, A. Gupta, A. A. Efros, Ensemble of Exemplar-SVMs for Object Detection and Beyond , ICCV 2011 http://www.cs.cmu.edu/~tmalisie/projects/iccv11/ Object Detection Viola-Jones Object Detection P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR, 2001 http://pr.willowgarage.com/wiki/FaceDetection Object Discovery Using Multiple Segmentations to Discover Objects and their Extent in Image Collections B. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, Using Multiple Segmentations to Discover Objects and their Extent in Image Collections, CVPR 2006 http://people.csail.mit.edu/brussell/research/proj/mult_seg_discovery/index.html Object Proposal Objectness measure B. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010 http://www.vision.ee.ethz.ch/~calvin/objectness/objectness-release-v1.01.tar.gz Object Proposal Parametric min-cut J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010 http://sminchisescu.ins.uni-bonn.de/code/cpmc/ Object Proposal Region-based Object Proposal I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010 http://vision.cs.uiuc.edu/proposals/ Object Recognition Recognition by Association via Learning Per-exemplar Distances T. Malisiewicz, A. A. Efros, Recognition by Association via Learning Per-exemplar Distances, CVPR 2008 http://www.cs.cmu.edu/~tmalisie/projects/cvpr08/dfuns.tar.gz Object Recognition Biologically motivated object recognition T. Serre, L. Wolf and T. Poggio. Object recognition with features inspired by visual cortex, CVPR 2005 http://cbcl.mit.edu/software-datasets/standardmodel/index.html Object Segmentation Geodesic Star Convexity for Interactive Image Segmentation V. Gulshan, C. Rother, A. Criminisi, A. Blake and A. Zisserman. Geodesic star convexity for interactive image segmentation http://www.robots.ox.ac.uk/~vgg/software/iseg/index.shtml Object Segmentation ClassCut for Unsupervised Class Segmentation B. Alexe, T. Deselaers and V. Ferrari, ClassCut for Unsupervised Class Segmentation, ECCV 2010 http://www.vision.ee.ethz.ch/~calvin/classcut/ClassCut-release.zip Object Segmentation Sparse to Dense Labeling P. Ochs, T. Brox, Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions, ICCV 2011 http://lmb.informatik.uni-freiburg.de/resources/binaries/SparseToDenseLabeling.tar.gz Optical Flow Optical Flow by Deqing Sun D. Sun, S. Roth, M. J. Black, Secrets of Optical Flow Estimation and Their Principles, CVPR, 2010 http://www.cs.brown.edu/~dqsun/code/flow_code.zip Optical Flow Classical Variational Optical Flow T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, ECCV 2004 http://lmb.informatik.uni-freiburg.de/resources/binaries/ Optical Flow Large Displacement Optical Flow T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, PAMI 2011 http://lmb.informatik.uni-freiburg.de/resources/binaries/ Optical Flow Dense Point Tracking N. Sundaram, T. Brox, K. Keutzer Dense point trajectories by GPU-accelerated large displacement optical flow, ECCV 2010 http://lmb.informatik.uni-freiburg.de/resources/binaries/ Optical Flow Optical Flow Evaluation S. Baker et al. A Database and Evaluation Methodology for Optical Flow, IJCV, 2011 http://vision.middlebury.edu/flow/ Optical Flow Horn and Schunck's Optical Flow   http://www.cs.brown.edu/~dqsun/code/hs.zip Optical Flow Black and Anandan's Optical Flow   http://www.cs.brown.edu/~dqsun/code/ba.zip Pose Estimation Training Deformable Models for Localization Ramanan, D. "Learning to Parse Images of Articulated Bodies." NIPS 2006 http://www.ics.uci.edu/~dramanan/papers/parse/index.html Pose Estimation Calvin Upper-Body Detector E. Marcin, F. Vittorio, Better Appearance Models for Pictorial Structures, BMVC 2009 http://www.vision.ee.ethz.ch/~calvin/calvin_upperbody_detector/ Pose Estimation Articulated Pose Estimation using Flexible Mixtures of Parts Y. Yang, D. Ramanan, Articulated Pose Estimation using Flexible Mixtures of Parts, CVPR 2011 http://phoenix.ics.uci.edu/software/pose/ Pose Estimation Estimating Human Pose from Occluded Images J.-B. Huang and M.-H. Yang, Estimating Human Pose from Occluded Images, ACCV 2009 http://faculty.ucmerced.edu/mhyang/code/accv09_pose.zip Saliency Detection Saliency detection: A spectral residual approach X. Hou and L. Zhang. Saliency detection: A spectral residual approach. CVPR, 2007 http://www.klab.caltech.edu/~xhou/projects/spectralResidual/spectralresidual.html Saliency Detection Saliency Using Natural statistics L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 2008 http://cseweb.ucsd.edu/~l6zhang/ Saliency Detection Attention via Information Maximization N. Bruce and J. Tsotsos. Saliency based on information maximization. In NIPS, 2005 http://www.cse.yorku.ca/~neil/AIM.zip Saliency Detection Itti, Koch, and Niebur' saliency detection L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 1998 http://www.saliencytoolbox.net/ Saliency Detection Frequency-tuned salient region detection R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009 http://ivrgwww.epfl.ch/supplementary_material/RK_CVPR09/index.html Saliency Detection Saliency-based video segmentation K. Fukuchi, K. Miyazato, A. Kimura, S. Takagi and J. Yamato, Saliency-based video segmentation with graph cuts and sequentially updated priors, ICME 2009 http://www.brl.ntt.co.jp/people/akisato/saliency3.html Saliency Detection Segmenting salient objects from images and videos E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos. CVPR, 2010 http://www.cse.oulu.fi/MVG/Downloads/saliency Saliency Detection Graph-based visual saliency J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007 http://www.klab.caltech.edu/~harel/share/gbvs.php Saliency Detection Learning to Predict Where Humans Look T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look, ICCV, 2009 http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html Saliency Detection Spectrum Scale Space based Visual Saliency J Li, M D. Levine, X An and H. He, Saliency Detection Based on Frequency and Spatial Domain Analyses, BMVC 2011 http://www.cim.mcgill.ca/~lijian/saliency.htm Saliency Detection Discriminant Saliency for Visual Recognition from Cluttered Scenes D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes, NIPS, 2004 http://www.svcl.ucsd.edu/projects/saliency/ Saliency Detection Context-aware saliency detection S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, 2010. http://webee.technion.ac.il/labs/cgm/Computer-Graphics-Multimedia/Software/Saliency/Saliency.html Saliency Detection Saliency detection using maximum symmetric surround R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 2010 http://ivrg.epfl.ch/supplementary_material/RK_ICIP2010/index.html Saliency Detection Global Contrast based Salient Region Detection M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection. CVPR, 2011 http://cg.cs.tsinghua.edu.cn/people/~cmm/saliency/ Saliency Detection Learning Hierarchical Image Representation with Sparsity, Saliency and Locality J. Yang and M.-H. Yang, Learning Hierarchical Image Representation with Sparsity, Saliency and Locality, BMVC 2011   Sparse Representation Centralized Sparse Representation for Image Restoration W. Dong, L. Zhang and G. Shi, “Centralized Sparse Representation for Image Restoration,” ICCV 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/CSR_IR.zip Sparse Representation Efficient sparse coding algorithms H. Lee, A. Battle, R. Rajat and A. Y. Ng, Efficient sparse coding algorithms, NIPS 2007 http://ai.stanford.edu/~hllee/softwares/nips06-sparsecoding.htm Sparse Representation Fisher Discrimination Dictionary Learning for Sparse Representation M. Yang, L. Zhang, X. Feng and D. Zhang, Fisher Discrimination Dictionary Learning for Sparse Representation, ICCV 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/FDDL.zip Sparse Representation Robust Sparse Coding for Face Recognition M. Yang, L. Zhang, J. Yang and D. Zhang, “Robust Sparse Coding for Face Recognition,” CVPR 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/RSC.zip Sparse Representation Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing http://www.cs.technion.ac.il/~elad/Various/Matlab-Package-Book.rar Sparse Representation SPArse Modeling Software J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online Learning for Matrix Factorization and Sparse Coding, JMLR 2010 http://www.di.ens.fr/willow/SPAMS/ Sparse Representation Sparse coding simulation software Olshausen BA, Field DJ, "Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images", Nature 1996 http://redwood.berkeley.edu/bruno/sparsenet/ Sparse Representation A Linear Subspace Learning Approach via Sparse Coding L. Zhang, P. Zhu, Q. Hu and D. Zhang, “A Linear Subspace Learning Approach via Sparse Coding,” ICCV 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/LSL_SC.zip Stereo Constant-Space Belief Propagation Q. Yang, L. Wang, and N. Ahuja, A Constant-Space Belief Propagation Algorithm for Stereo Matching, CVPR 2010 http://www.cs.cityu.edu.hk/~qiyang/publications/code/cvpr-10-csbp/csbp.htm Stereo Stereo Evaluation D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV 2001 http://vision.middlebury.edu/stereo/ Image Denoising andStereo Matching Efficient Belief Propagation for Early Vision P. F. Felzenszwalb and D. P. Huttenlocher, Efficient Belief Propagation for Early Vision, IJCV, 2006 http://www.cs.brown.edu/~pff/bp/ Structure from motion Nonrigid Structure From Motion in Trajectory Space   http://cvlab.lums.edu.pk/nrsfm/index.html Structure from motion libmv   http://code.google.com/p/libmv/ Structure from motion Bundler N. Snavely, S M. Seitz, R Szeliski. Photo Tourism: Exploring image collections in 3D. SIGGRAPH 2006 http://phototour.cs.washington.edu/bundler/ Structure from motion FIT3D   http://www.fit3d.info/ Structure from motion VisualSFM : A Visual Structure from Motion System   http://www.cs.washington.edu/homes/ccwu/vsfm/ Structure from motion OpenSourcePhotogrammetry   http://opensourcephotogrammetry.blogspot.com/ Structure from motion Structure and Motion Toolkit in Matlab   http://cms.brookes.ac.uk/staff/PhilipTorr/Code/code_page_4.htm Structure from motion Structure from Motion toolbox for Matlab by Vincent Rabaud   http://code.google.com/p/vincents-structure-from-motion-matlab-toolbox/ Subspace Learning Generalized Principal Component Analysis R. Vidal, Y. Ma and S. Sastry. Generalized Principal Component Analysis (GPCA), CVPR 2003 http://www.vision.jhu.edu/downloads/main.php?dlID=c1 Text Recognition Text recognition in the wild K. Wang, B. Babenko, and S. Belongie, End-to-end Scene Text Recognition, ICCV 2011 http://vision.ucsd.edu/~kai/grocr/ Text Recognition Neocognitron for handwritten digit recognition K. Fukushima: "Neocognitron for handwritten digit recognition", Neurocomputing, 2003 http://visiome.neuroinf.jp/modules/xoonips/detail.php?item_id=375 Texture Synthesis Image Quilting for Texture Synthesis and Transfer A. A. Efros and W. T. Freeman, Image Quilting for Texture Synthesis and Transfer, SIGGRAPH 2001 http://www.cs.cmu.edu/~efros/quilt_research_code.zip Visual Tracking GPU Implementation of Kanade-Lucas-Tomasi Feature Tracker S. N Sinha, J.-M. Frahm, M. Pollefeys and Y. Genc, Feature Tracking and Matching in Video Using Programmable Graphics Hardware, MVA, 2007 http://cs.unc.edu/~ssinha/Research/GPU_KLT/ Visual Tracking Superpixel Tracking S. Wang, H. Lu, F. Yang, and M.-H. Yang, Superpixel Tracking, ICCV 2011 http://faculty.ucmerced.edu/mhyang/papers/iccv11a.html Visual Tracking Tracking with Online Multiple Instance Learning B. Babenko, M.-H. Yang, S. Belongie, Visual Tracking with Online Multiple Instance Learning, PAMI 2011 http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml Visual Tracking Motion Tracking in Image Sequences C. Stauffer and W. E. L. Grimson. Learning patterns of activity using real-time tracking, PAMI, 2000 http://www.cs.berkeley.edu/~flw/tracker/ Visual Tracking L1 Tracking X. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009 http://www.dabi.temple.edu/~hbling/code_data.htm Visual Tracking Online Discriminative Object Tracking with Local Sparse Representation Q. Wang, F. Chen, W. Xu, and M.-H. Yang, Online Discriminative Object Tracking with Local Sparse Representation, WACV 2012 http://faculty.ucmerced.edu/mhyang/code/wacv12a_code.zip Visual Tracking KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker B. D. Lucas and T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. IJCAI, 1981 http://www.ces.clemson.edu/~stb/klt/ Visual Tracking Online boosting trackers H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR, 2006 http://www.vision.ee.ethz.ch/boostingTrackers/ Visual Tracking Visual Tracking Decomposition J Kwon and K. M. Lee, Visual Tracking Decomposition, CVPR 2010 http://cv.snu.ac.kr/research/~vtd/ Visual Tracking Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects H. Pirsiavash, D. Ramanan, C. Fowlkes. "Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects, CVPR 2011 http://www.ics.uci.edu/~hpirsiav/papers/tracking_cvpr11_release_v1.0.tar.gz Visual Tracking Lucas-Kanade affine template tracking S. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, IJCV 2002 http://www.mathworks.com/matlabcentral/fileexchange/24677-lucas-kanade-affine-template-tracking Visual Tracking Object Tracking A. Yilmaz, O. Javed and M. Shah, Object Tracking: A Survey, ACM Journal of Computing Surveys, Vol. 38, No. 4, 2006 http://plaza.ufl.edu/lvtaoran/object%20tracking.htm Visual Tracking Visual Tracking with Histograms and Articulating Blocks S. M. Shshed Nejhum, J. Ho, and M.-H.Yang, Visual Tracking with Histograms and Articulating Blocks, CVPR 2008 http://www.cise.ufl.edu/~smshahed/tracking.htm Visual Tracking Tracking using Pixel-Wise Posteriors C. Bibby and I. Reid, Tracking using Pixel-Wise Posteriors, ECCV 2008 http://www.robots.ox.ac.uk/~cbibby/research_pwp.shtml Visual Tracking Incremental Learning for Robust Visual Tracking D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007 http://www.cs.toronto.edu/~dross/ivt/ Visual Tracking Particle Filter Object Tracking   http://blogs.oregonstate.edu/hess/code/particles/

Other 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 The PASCAL Visual Object Classes Computer vision dataset from CMU
Lectures	Videolectures
Source Codes	Computer Vision Algorithm Implementations OpenCV Source Code Collection for Reproducible Research
Patents	United States Patent & Trademark Office
Source Codes	Computer Vision Algorithm Implementations OpenCV Source Code Collection for Reproducible Research