计算机视觉算法的开源代码

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NameReferenceLinkFeature Detection,Feature Extraction, and Action RecognitionSpace-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 2005http://www.irisa.fr/vista/Equipe/People/Laptev/download/stip-1.1-winlinux.zip andhttp://www.nada.kth.se/cvap/abstracts/cvap284.htmlAction Recognition3D Gradients (HOG3D)A. Klaser, M. Marszałek, and C. Schmid, BMVC, 2008.http://lear.inrialpes.fr/people/klaeser/research_hog3dAction RecognitionDense Trajectories Video DescriptionH. Wang and A. Klaser and C. Schmid and C.- L. Liu, Action Recognition by Dense Trajectories, CVPR, 2011http://lear.inrialpes.fr/people/wang/dense_trajectoriesAlpha MattingSpectral MattingA. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008http://www.vision.huji.ac.il/SpectralMatting/Alpha MattingShared MattingE. S. L. Gastal and M. M. Oliveira, Computer Graphics Forum, 2010http://www.inf.ufrgs.br/~eslgastal/SharedMatting/Alpha MattingBayesian MattingY. Y. Chuang, B. Curless, D. H. Salesin, and R. Szeliski, A Bayesian Approach to Digital Matting, CVPR, 2001http://www1.idc.ac.il/toky/CompPhoto-09/Projects/Stud_projects/Miki/index.htmlAlpha MattingClosed Form MattingA. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting, PAMI 2008.http://people.csail.mit.edu/alevin/matting.tar.gzAlpha MattingLearning-based MattingY. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009http://www.mathworks.com/matlabcentral/fileexchange/31412Camera CalibrationCamera Calibration Toolbox for Matlabhttp://www.vision.caltech.edu/bouguetj/calib_doc/htmls/ref.htmlhttp://www.vision.caltech.edu/bouguetj/calib_doc/Camera CalibrationEasyCamCalibJ. Barreto, J. Roquette, P. Sturm, and F. Fonseca, Automatic camera calibration applied to medical endoscopy, BMVC, 2009http://arthronav.isr.uc.pt/easycamcalib/Camera CalibrationEpipolar Geometry ToolboxG.L. Mariottini, D. Prattichizzo, EGT: a Toolbox for Multiple View Geometry and Visual Servoing, IEEE Robotics & Automation Magazine, 2005http://egt.dii.unisi.it/ClusteringSpectral Clustering - UW Project http://www.stat.washington.edu/spectral/ClusteringSpectral Clustering - UCSD Project http://vision.ucsd.edu/~sagarwal/spectral-0.2.tgzClusteringSelf-Tuning Spectral Clustering http://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.htmlClusteringK-Means - Oxford Code http://www.cs.ucf.edu/~vision/Code/vggkmeans.zipClusteringK-Means - VLFeat http://www.vlfeat.org/Common Visual Pattern DiscoverySketching the CommonS. Bagon, O. Brostovsky, M. Galun and M. Irani, Detecting and Sketching the Common, CVPR 2010http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/SketchCommonCVPR10_v1.1.tar.gzCommon Visual Pattern DiscoveryCommon Visual Pattern Discovery via Spatially Coherent CorrespondencesH. Liu, S. Yan, "Common Visual Pattern Discovery via Spatially Coherent Correspondences", CVPR 2010https://sites.google.com/site/lhrbss/home/papers/SimplifiedCode.zip?attredirects=0Density EstimationKernel Density Estimation Toolbox http://www.ics.uci.edu/~ihler/code/kde.htmlDepth SensorKinect SDKhttp://www.microsoft.com/en-us/kinectforwindows/http://www.microsoft.com/en-us/kinectforwindows/Dimension ReductionISOMAP http://isomap.stanford.edu/Dimension ReductionLLE http://www.cs.nyu.edu/~roweis/lle/code.htmlDimension ReductionLaplacian Eigenmaps http://www.cse.ohio-state.edu/~mbelkin/algorithms/Laplacian.tarDimension ReductionDiffusion maps http://www.stat.cmu.edu/~annlee/software.htmDimension ReductionDimensionality Reduction Toolbox http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.htmlDistance Metric LearningMatlab Toolkit for Distance Metric Learning http://www.cs.cmu.edu/~liuy/distlearn.htmDistance TransformationDistance Transforms of Sampled Functions http://people.cs.uchicago.edu/~pff/dt/Feature DetectionCanny Edge DetectionJ. Canny, A Computational Approach To Edge Detection, PAMI, 1986http://www.mathworks.com/help/toolbox/images/ref/edge.htmlFeature DetectionFAST Corner DetectionE. Rosten and T. Drummond, Machine learning for high-speed corner detection, ECCV, 2006http://www.edwardrosten.com/work/fast.htmlFeature DetectionEdge Foci Interest PointsL. Zitnickand K. Ramnath, Edge Foci Interest Points, ICCV, 2011http://research.microsoft.com/en-us/um/people/larryz/edgefoci/edge_foci.htmFeature DetectionBoundary Preserving Dense Local RegionsJ. Kim and K. Grauman, Boundary Preserving Dense Local Regions, CVPR 2011http://vision.cs.utexas.edu/projects/bplr/bplr.htmlFeature ExtractionBRIEF: Binary Robust Independent Elementary FeaturesM. Calonder, V. Lepetit, C. Strecha, P. Fua, BRIEF: Binary Robust Independent Elementary Features, ECCV 2010http://cvlab.epfl.ch/research/detect/brief/Feature Detection andFeature ExtractionScale-invariant feature transform (SIFT) - VLFeatD. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.http://www.vlfeat.org/Feature Detection andFeature ExtractionScale-invariant feature transform (SIFT) - Demo SoftwareD. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.http://www.cs.ubc.ca/~lowe/keypoints/Feature ExtractionGlobal and Efficient Self-SimilarityT. 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 2010http://www.vision.ee.ethz.ch/~calvin/gss/selfsim_release1.0.tgzFeature Detection andFeature ExtractionAffine-SIFTJ.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009http://www.ipol.im/pub/algo/my_affine_sift/Feature Detection andFeature ExtractionGeometric BlurA. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR, 2005http://www.robots.ox.ac.uk/~vgg/software/MKL/Feature ExtractionPCA-SIFTY. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004http://www.cs.cmu.edu/~yke/pcasift/Feature Detection andFeature ExtractionScale-invariant feature transform (SIFT) - LibraryD. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.http://blogs.oregonstate.edu/hess/code/sift/Feature Detection andFeature ExtractionGroups of Adjacent Contour SegmentsV. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, Groups of Adjacent Contour Segments for Object Detection, PAMI, 2007http://www.robots.ox.ac.uk/~vgg/share/ferrari/release-kas-v102.tgzFeature Detection andFeature ExtractionSpeeded Up Robust Feature (SURF) - Matlab WrapperH. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006http://www.maths.lth.se/matematiklth/personal/petter/surfmex.phpFeature ExtractionShape ContextS. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sc_digits.htmlFeature Detection andFeature ExtractionSpeeded Up Robust Feature (SURF) - Open SURFH. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006http://www.chrisevansdev.com/computer-vision-opensurf.htmlFeature Detection andFeature ExtractionMaximally stable extremal regions (MSER)J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002http://www.robots.ox.ac.uk/~vgg/research/affine/Feature ExtractionGIST DescriptorA. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, IJCV, 2001http://people.csail.mit.edu/torralba/code/spatialenvelope/Feature Detection andFeature ExtractionColor DescriptorK. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene Recognition, PAMI, 2010http://koen.me/research/colordescriptors/Feature ExtractionLocal Self-Similarity DescriptorE. Shechtman and M. Irani. Matching local self-similarities across images and videos, CVPR, 2007http://www.robots.ox.ac.uk/~vgg/software/SelfSimilarity/Feature Detection andFeature ExtractionMaximally stable extremal regions (MSER) - VLFeatJ. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002http://www.vlfeat.org/Feature ExtractionPyramids of Histograms of Oriented Gradients (PHOG)A. Bosch, A. Zisserman, and X. Munoz, Representing shape with a spatial pyramid kernel, CIVR, 2007http://www.robots.ox.ac.uk/~vgg/research/caltech/phog/phog.zipFeature Detection andFeature ExtractionAffine Covariant FeaturesT. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors: A Survey, Foundations and Trends in Computer Graphics and Vision, 2008http://www.robots.ox.ac.uk/~vgg/research/affine/Feature ExtractionsRD-SIFTM. Lourenco, J. P. Barreto and A. Malti, Feature Detection and Matching in Images with Radial Distortion, ICRA 2010http://arthronav.isr.uc.pt/~mlourenco/srdsift/index.html#Graph MatchingReweighted Random Walks for Graph MatchingM. Cho, J. Lee, and K. M. Lee, Reweighted Random Walks for Graph Matching, ECCV 2010http://cv.snu.ac.kr/research/~RRWM/Graph MatchingHyper-graph Matching via Reweighted Random WalksJ. Lee, M. Cho, K. M. Lee. "Hyper-graph Matching via Reweighted Random Walks", CVPR 2011http://cv.snu.ac.kr/research/~RRWHM/Illumination, Reflectance, and ShadowWebcam Clip Art: Appearance and Illuminant Transfer from Time-lapse SequencesJ-F. Lalonde, A. A. Efros, S. G. Narasimhan, Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse Sequences, SIGGRAPH Asia 2009http://www.cs.cmu.edu/~jlalonde/software.html#skyModelIllumination, Reflectance, and ShadowGround shadow detectionJ.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs, ECCV 2010http://www.jflalonde.org/software.html#shadowDetectionIllumination, Reflectance, and ShadowShadow Detection using Paired RegionR. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011http://www.cs.illinois.edu/homes/guo29/projects/shadow.htmlIllumination, Reflectance, and ShadowReal-time Specular Highlight RemovalQ. Yang, S. Wang and N. Ahuja, Real-time Specular Highlight Removal Using Bilateral Filtering, ECCV 2010http://www.cs.cityu.edu.hk/~qiyang/publications/code/eccv-10.zipIllumination, Reflectance, and ShadowEstimating Natural Illumination from a Single Outdoor ImageJ-F. Lalonde, A. A. Efros, S. G. Narasimhan, Estimating Natural Illumination from a Single Outdoor Image , ICCV 2009http://www.cs.cmu.edu/~jlalonde/software.html#skyModelIllumination, Reflectance, and ShadowWhat 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 2008http://www.cs.cmu.edu/~jlalonde/software.html#skyModelImage ClassificationLocality-constrained Linear CodingJ. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification, CVPR, 2010http://www.ifp.illinois.edu/~jyang29/LLC.htmImage ClassificationSparse Coding for Image ClassificationJ. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification, CVPR, 2009http://www.ifp.illinois.edu/~jyang29/ScSPM.htmImage ClassificationTexture ClassificationM. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005http://www.robots.ox.ac.uk/~vgg/research/texclass/index.htmlFeature Matching andImage ClassificationThe Pyramid Match: Efficient Matching for Retrieval and RecognitionK. Grauman and T. Darrell. The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005http://www.cs.utexas.edu/~grauman/research/projects/pmk/pmk_projectpage.htmImage ClassificationSpatial Pyramid MatchingS. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, CVPR 2006http://www.cs.unc.edu/~lazebnik/research/SpatialPyramid.zipImage DeblurringRadon TransformT. S. Cho, S. Paris, B. K. P. Horn, W. T. Freeman, Blur kernel estimation using the radon transform, CVPR 2011http://people.csail.mit.edu/taegsang/Documents/RadonDeblurringCode.zipImage DeblurringAnalyzing spatially varying blurA. Chakrabarti, T. Zickler, and W. T. Freeman, Analyzing Spatially-varying Blur, CVPR 2010http://www.eecs.harvard.edu/~ayanc/svblur/Image Denoising,Image Super-resolution, and Image DeblurringLearning Models of Natural Image PatchesD. Zoran and Y. Weiss, From Learning Models of Natural Image Patches to Whole Image Restoration, ICCV, 2011http://www.cs.huji.ac.il/~daniez/Image DeblurringNon-blind deblurring (and blind denoising) with integrated noise estimationU. Schmidt, K. Schelten, and S. Roth. Bayesian deblurring with integrated noise estimation, CVPR 2011http://www.gris.tu-darmstadt.de/research/visinf/software/index.en.htmImage DeblurringEficient Marginal Likelihood Optimization in Blind DeconvolutionA. Levin, Y. Weiss, F. Durand, W. T. Freeman. Efficient Marginal Likelihood Optimization in Blind Deconvolution, CVPR 2011http://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR2011Code.zipImage DeblurringRichardson-Lucy Deblurring for Scenes under Projective Motion PathY.-W. Tai, P. Tan, M. S. Brown: Richardson-Lucy Deblurring for Scenes under Projective Motion Path, PAMI 2011http://yuwing.kaist.ac.kr/projects/projectivedeblur/projectivedeblur_files/ProjectiveDeblur.zipImage DenoisingSparsity-based Image DenoisingW. Dong, X. Li, L. Zhang and G. Shi, Sparsity-based Image Denoising vis Dictionary Learning and Structural Clustering, CVPR, 2011http://www.csee.wvu.edu/~xinl/CSR.htmlImage DenoisingK-SVD http://www.cs.technion.ac.il/~ronrubin/Software/ksvdbox13.zipImage DenoisingClustering-based DenoisingP. Chatterjee and P. Milanfar, Clustering-based Denoising with Locally Learned Dictionaries (K-LLD), TIP, 2009http://users.soe.ucsc.edu/~priyam/K-LLD/Image DenoisingBLS-GSM http://decsai.ugr.es/~javier/denoise/Image DenoisingField of Experts http://www.cs.brown.edu/~roth/research/software.htmlImage DenoisingNon-local Means http://dmi.uib.es/~abuades/codis/NLmeansfilter.mImage DenoisingWhat makes a good model of natural images ?Y. Weiss and W. T. Freeman, CVPR 2007http://www.cs.huji.ac.il/~yweiss/BRFOE.zipImage DenoisingBM3D http://www.cs.tut.fi/~foi/GCF-BM3D/Image DenoisingKernel Regressions http://www.soe.ucsc.edu/~htakeda/MatlabApp/KernelRegressionBasedImageProcessingToolBox_ver1-1beta.zipImage DenoisingGaussian Field of Experts http://www.cs.huji.ac.il/~yweiss/BRFOE.zipImage DenoisingNonlocal means with cluster treesT. Brox, O. Kleinschmidt, D. Cremers, Efficient nonlocal means for denoising of textural patterns, TIP 2008http://lmb.informatik.uni-freiburg.de/resources/binaries/nlmeans_brox_tip08Linux64.zipImage FilteringGradientShopP. Bhat, C.L. Zitnick, M. Cohen, B. Curless, and J. Kim, GradientShop: A Gradient-Domain Optimization Framework for Image and Video Filtering, TOG 2010http://grail.cs.washington.edu/projects/gradientshop/Image FilteringWeighted Least Squares FilterZ. Farbman, R. Fattal, D. Lischinski, R. Szeliski, Edge-Preserving Decompositions for Multi-Scale Tone and Detail Manipulation, SIGGRAPH 2008http://www.cs.huji.ac.il/~danix/epd/Image FilteringReal-time O(1) Bilateral FilteringQ. Yang, K.-H. Tan and N. Ahuja, Real-time O(1) Bilateral Filtering, CVPR 2009http://vision.ai.uiuc.edu/~qyang6/publications/code/qx_constant_time_bilateral_filter_ss.zipImage FilteringGuided Image FilteringK. He, J. Sun, X. Tang, Guided Image Filtering, ECCV 2010http://personal.ie.cuhk.edu.hk/~hkm007/eccv10/guided-filter-code-v1.rarImage FilteringFast Bilateral FilterS. Paris and F. Durand, A Fast Approximation of the Bilateral Filter using a Signal Processing Approach, ECCV, 2006http://people.csail.mit.edu/sparis/bf/Image FilteringImage smoothing via L0 Gradient MinimizationL. Xu, C. Lu, Y. Xu, J. Jia, Image smoothing via L0 Gradient Minimization, SIGGRAPH Asia 2011http://www.cse.cuhk.edu.hk/~leojia/projects/L0smoothing/L0smoothing.zipImage FilteringDomain TransformationE. Gastal, M. Oliveira, Domain Transform for Edge-Aware Image and Video Processing, SIGGRAPH 2011http://inf.ufrgs.br/~eslgastal/DomainTransform/DomainTransformFilters-Source-v1.0.zipImage Processing andImage FilteringPiotr's Image & Video Matlab ToolboxPiotr Dollar, Piotr's Image & Video Matlab Toolbox, http://vision.ucsd.edu/~pdollar/toolbox/doc/index.htmlhttp://vision.ucsd.edu/~pdollar/toolbox/doc/index.htmlImage FilteringLocal Laplacian FiltersS. Paris, S. Hasinoff, J. Kautz, Local Laplacian Filters: Edge-Aware Image Processing with a Laplacian Pyramid, SIGGRAPH 2011http://people.csail.mit.edu/sparis/publi/2011/siggraph/matlab_source_code.zipImage FilteringSVM for Edge-Preserving FilteringQ. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering, CVPR 2010http://vision.ai.uiuc.edu/~qyang6/publications/code/cvpr-10-svmbf/program_video_conferencing.zipImage FilteringAnisotropic DiffusionP. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, PAMI 1990http://www.mathworks.com/matlabcentral/fileexchange/14995-anisotropic-diffusion-perona-malikImage Quality AssessmentSPIQA http://vision.ai.uiuc.edu/~bghanem2/shared_code/SPIQA_code.zipImage Quality AssessmentDegradation Model http://users.ece.utexas.edu/~bevans/papers/2000/imageQuality/index.htmlImage Quality AssessmentFeature SIMilarity Index http://www4.comp.polyu.edu.hk/~cslzhang/IQA/FSIM/FSIM.htmImage Quality AssessmentStructural SIMilarity https://ece.uwaterloo.ca/~z70wang/research/ssim/Image SegmentationSegmentation by Minimum Code LengthA. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression, CVIU, 2007http://perception.csl.uiuc.edu/coding/image_segmentation/Image SegmentationNormalized CutJ. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000http://www.cis.upenn.edu/~jshi/software/Image SegmentationEntropy Rate Superpixel SegmentationM.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011http://www.umiacs.umd.edu/~mingyliu/src/ers_matlab_wrapper_v0.1.zipImage SegmentationMean-Shift Image Segmentation - EDISOND. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002http://coewww.rutgers.edu/riul/research/code/EDISON/index.htmlImage SegmentationEfficient Graph-based Image Segmentation - Matlab WrapperP. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004http://www.mathworks.com/matlabcentral/fileexchange/25866-efficient-graph-based-image-segmentationImage SegmentationBiased Normalized CutS. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut, CVPR 2011http://www.cs.berkeley.edu/~smaji/projects/biasedNcuts/Image SegmentationMultiscale Segmentation TreeE. 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 1996http://vision.ai.uiuc.edu/segmentationImage SegmentationEfficient Graph-based Image Segmentation - C++ codeP. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004http://people.cs.uchicago.edu/~pff/segment/Image SegmentationSuperpixel by Gerg MoriX. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003http://www.cs.sfu.ca/~mori/research/superpixels/Image SegmentationSegmenting Scenes by Matching Image CompositesB. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, NIPS 2009http://www.cs.washington.edu/homes/bcr/projects/SceneComposites/index.htmlImage SegmentationRecovering Occlusion Boundaries from a Single ImageD. 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 SegmentationQuick-ShiftA. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking, ECCV, 2008http://www.vlfeat.org/overview/quickshift.htmlImage SegmentationSLIC SuperpixelsR. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010http://ivrg.epfl.ch/supplementary_material/RK_SLICSuperpixels/index.htmlImage SegmentationMean-Shift Image Segmentation - Matlab WrapperD. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/edison_matlab_interface.tar.gzImage SegmentationOWT-UCM Hierarchical SegmentationP. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.htmlImage SegmentationTurbepixelsA. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric Flows, PAMI 2009http://www.cs.toronto.edu/~babalex/research.htmlImage Super-resolutionMRF for image super-resolutionW. 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, 2011http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.htmlImage Super-resolutionSingle-Image Super-Resolution Matlab PackageR. Zeyde, M. Elad, and M. Protter, On Single Image Scale-Up using Sparse-Representations, LNCS 2010http://www.cs.technion.ac.il/~elad/Various/Single_Image_SR.zipImage Super-resolutionSelf-Similarities for Single Frame Super-ResolutionC.-Y. Yang, J.-B. Huang, and M.-H. Yang, Exploiting Self-Similarities for Single Frame Super-Resolution, ACCV 2010https://eng.ucmerced.edu/people/cyang35/ACCV10.zipImage Super-resolutionMDSP Resolution Enhancement SoftwareS. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Fast and Robust Multi-frame Super-resolution, TIP 2004http://users.soe.ucsc.edu/~milanfar/software/superresolution.htmlImage Super-resolutionSprarse coding super-resolutionJ. Yang, J. Wright, T. S. Huang, and Y. Ma. Image super-resolution via sparse representation, TIP 2010http://www.ifp.illinois.edu/~jyang29/ScSR.htmImage Super-resolutionMulti-frame image super-resolutionPickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesishttp://www.robots.ox.ac.uk/~vgg/software/SR/index.htmlImage UnderstandingSuperParsingJ. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image Parsing with Superpixels, ECCV 2010http://www.cs.unc.edu/~jtighe/Papers/ECCV10/eccv10-jtighe-code.zipImage UnderstandingDiscriminative Models for Multi-Class Object LayoutC. Desai, D. Ramanan, C. Fowlkes. "Discriminative Models for Multi-Class Object Layout, IJCV 2011http://www.ics.uci.edu/~desaic/multiobject_context.zipImage UnderstandingNonparametric Scene Parsing via Label TransferC. Liu, J. Yuen, and Antonio Torralba, Nonparametric Scene Parsing via Label Transfer, PAMI 2011http://people.csail.mit.edu/celiu/LabelTransfer/index.htmlImage UnderstandingBlocks World Revisited: Image Understanding using Qualitative Geometry and MechanicsA. Gupta, A. A. Efros, M. Hebert, Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics, ECCV 2010http://www.cs.cmu.edu/~abhinavg/blocksworld/#downloadsImage UnderstandingTowards Total Scene UnderstandingL.-J. Li, R. Socher and Li F.-F.. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework, CVPR 2009http://vision.stanford.edu/projects/totalscene/index.htmlImage UnderstandingObject BankLi-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 2010http://vision.stanford.edu/projects/objectbank/index.htmlKernels and DistancesFast Directional Chamfer Matching http://www.umiacs.umd.edu/~mingyliu/src/fdcm_matlab_wrapper_v0.2.zipKernels and DistancesEfficient 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 2007http://www.dabi.temple.edu/~hbling/code/EmdL1_v3.zipKernels and DistancesDiffusion-based distanceH. Ling and K. Okada, Diffusion Distance for Histogram Comparison, CVPR 2006http://www.dabi.temple.edu/~hbling/code/DD_v1.zipLow-Rank ModelingTILT: Transform Invariant Low-rank TexturesZ. Zhang, A. Ganesh, X. Liang, and Y. Ma, TILT: Transform Invariant Low-rank Textures, IJCV 2011http://perception.csl.uiuc.edu/matrix-rank/tilt.htmlLow-Rank ModelingLow-Rank Matrix Recovery and Completion http://perception.csl.uiuc.edu/matrix-rank/sample_code.htmlLow-Rank ModelingRASL: Robust Batch Alignment of Images by Sparse and Low-Rank DecompositionY. Peng, A. Ganesh, J. Wright, W. Xu, and Y. Ma, RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition, CVPR 2010http://perception.csl.uiuc.edu/matrix-rank/rasl.htmlMRF OptimizationMRF Minimization EvaluationR. Szeliski et al., A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, PAMI, 2008http://vision.middlebury.edu/MRF/MRF OptimizationMax-flow/min-cut for shape fittingV. Lempitsky and Y. Boykov, Global Optimization for Shape Fitting, CVPR 2007http://www.csd.uwo.ca/faculty/yuri/Implementations/TouchExpand.zipMRF OptimizationMax-flow/min-cutY. Boykov and V. Kolmogorov, An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, PAMI 2004http://vision.csd.uwo.ca/code/maxflow-v3.01.zipMRF OptimizationPlanar Graph CutF. R. Schmidt, E. Toppe and D. Cremers, Efﬁcient Planar Graph Cuts with Applications in Computer Vision, CVPR 2009http://vision.csd.uwo.ca/code/PlanarCut-v1.0.zipMRF OptimizationMax-flow/min-cut for massive gridsA. Delong and Y. Boykov, A Scalable Graph-Cut Algorithm for N-D Grids, CVPR 2008http://vision.csd.uwo.ca/code/regionpushrelabel-v1.03.zipMRF OptimizationMulti-label optimizationY. Boykov, O. Verksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001http://vision.csd.uwo.ca/code/gco-v3.0.zipMachine LearningStatistical Pattern Recognition ToolboxM.I. Schlesinger, V. Hlavac: Ten lectures on the statistical and structural pattern recognition, Kluwer Academic Publishers, 2002http://cmp.felk.cvut.cz/cmp/software/stprtool/Machine LearningNetlab Neural Network SoftwareC. M. Bishop, Neural Networks for Pattern RecognitionㄝOxford University Press, 1995http://www1.aston.ac.uk/eas/research/groups/ncrg/resources/netlab/Machine LearningBoosting Resources by Liangliang Caohttp://www.ifp.illinois.edu/~cao4/reading/boostingbib.htmhttp://www.ifp.illinois.edu/~cao4/reading/boostingbib.htmMachine LearningFastICA package for MATLABhttp://research.ics.tkk.fi/ica/book/http://research.ics.tkk.fi/ica/fastica/Multi-View StereoPatch-based Multi-view Stereo SoftwareY. Furukawa and J. Ponce, Accurate, Dense, and Robust Multi-View Stereopsis, PAMI 2009http://grail.cs.washington.edu/software/pmvs/Multi-View StereoClustering Views for Multi-view StereoY. Furukawa, B. Curless, S. M. Seitz, and R. Szeliski, Towards Internet-scale Multi-view Stereo, CVPR 2010http://grail.cs.washington.edu/software/cmvs/Multi-View StereoMulti-View Stereo EvaluationS. Seitz et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006http://vision.middlebury.edu/mview/Multiple Instance LearningDD-SVMYixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 2004 Multiple Instance LearningMIForestsC. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010http://www.ymer.org/amir/software/milforests/Multiple Instance LearningMILISZ. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010 Multiple Instance LearningMILESY. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection. PAMI 2006http://infolab.stanford.edu/~wangz/project/imsearch/SVM/PAMI06/Multiple Kernel LearningSHOGUNS. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning. JMLR, 2006http://www.shogun-toolbox.org/Multiple Kernel LearningOpenKernel.orgF. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011http://www.openkernel.org/Multiple Kernel LearningSimpleMKLA. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl. JMRL, 2008http://asi.insa-rouen.fr/enseignants/~arakotom/code/mklindex.htmlMultiple Kernel LearningDOGMAF. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010http://dogma.sourceforge.net/Multiple View GeometryMATLAB and Octave Functions for Computer Vision and Image ProcessingP. D. Kovesi. MATLAB and Octave Functions for Computer Vision and Image Processing, http://www.csse.uwa.edu.au/~pk/research/matlabfnshttp://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.htmlMultiple View GeometryMatlab Functions for Multiple View Geometry http://www.robots.ox.ac.uk/~vgg/hzbook/code/Nearest Neighbors MatchingANN: Approximate Nearest Neighbor Searching http://www.cs.umd.edu/~mount/ANN/Nearest Neighbors MatchingSpectral HashingY. Weiss, A. Torralba, R. Fergus, Spectral Hashing, NIPS 2008http://www.cs.huji.ac.il/~yweiss/SpectralHashing/Nearest Neighbors MatchingCoherency Sensitive HashingS. Korman, S. Avidan, Coherency Sensitive Hashing, ICCV 2011http://www.eng.tau.ac.il/~simonk/CSH/index.htmlNearest Neighbors MatchingFLANN: Fast Library for Approximate Nearest Neighbors http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANNNearest Neighbors MatchingLDAHash: Binary Descriptors for Matching in Large Image DatabasesC. 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.phpObject DetectionPoseletL. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009http://www.eecs.berkeley.edu/~lbourdev/poselets/Object DetectionCascade Object Detection with Deformable Part ModelsP. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models. CVPR, 2010http://people.cs.uchicago.edu/~rbg/star-cascade/Object DetectionMultiple KernelsA. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection. ICCV, 2009http://www.robots.ox.ac.uk/~vgg/software/MKL/Object DetectionHough Forests for Object DetectionJ. Gall and V. Lempitsky, Class-Speciﬁc Hough Forests for Object Detection, CVPR, 2009http://www.vision.ee.ethz.ch/~gallju/projects/houghforest/index.htmlObject DetectionDiscriminatively Trained Deformable Part ModelsP. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models, PAMI, 2010http://people.cs.uchicago.edu/~pff/latent/Feature Extraction andObject DetectionHistogram of Oriented Graidents - OLT for windowsN. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005http://www.computing.edu.au/~12482661/hog.htmlFeature Extraction andObject DetectionHistogram of Oriented Graidents - INRIA Object Localization ToolkitN. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005http://www.navneetdalal.com/softwareObject DetectionRecognition using regionsC. Gu, J. J. Lim, P. Arbelaez, and J. Malik, CVPR 2009http://www.cs.berkeley.edu/~chunhui/publications/cvpr09_v2.zipObject DetectionA simple parts and structure object detectorICCV 2005 short courses on Recognizing and Learning Object Categorieshttp://people.csail.mit.edu/fergus/iccv2005/partsstructure.htmlObject DetectionFeature CombinationP. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009http://www.vision.ee.ethz.ch/~pgehler/projects/iccv09/index.htmlObject DetectionEnsemble of Exemplar-SVMsT. Malisiewicz, A. Gupta, A. Efros. Ensemble of Exemplar-SVMs for Object Detection and Beyond . ICCV, 2011http://www.cs.cmu.edu/~tmalisie/projects/iccv11/Object DetectionA simple object detector with boostingICCV 2005 short courses on Recognizing and Learning Object Categorieshttp://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.htmlObject DetectionMax-Margin Hough TransformS. Maji and J. Malik, Object Detection Using a Max-Margin Hough Transform. CVPR 2009http://www.cs.berkeley.edu/~smaji/projects/max-margin-hough/Object DetectionImplicit Shape ModelB. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation, IJCV, 2008http://www.vision.ee.ethz.ch/~bleibe/code/ism.htmlObject DetectionEnsemble of Exemplar-SVMs for Object Detection and BeyondT. Malisiewicz, A. Gupta, A. A. Efros, Ensemble of Exemplar-SVMs for Object Detection and Beyond , ICCV 2011http://www.cs.cmu.edu/~tmalisie/projects/iccv11/Object DetectionViola-Jones Object DetectionP. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR, 2001http://pr.willowgarage.com/wiki/FaceDetectionObject DiscoveryUsing Multiple Segmentations to Discover Objects and their Extent in Image CollectionsB. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, Using Multiple Segmentations to Discover Objects and their Extent in Image Collections, CVPR 2006http://people.csail.mit.edu/brussell/research/proj/mult_seg_discovery/index.htmlObject ProposalObjectness measureB. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010http://www.vision.ee.ethz.ch/~calvin/objectness/objectness-release-v1.01.tar.gzObject ProposalParametric min-cutJ. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010http://sminchisescu.ins.uni-bonn.de/code/cpmc/Object ProposalRegion-based Object ProposalI. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010http://vision.cs.uiuc.edu/proposals/Object RecognitionRecognition by Association via Learning Per-exemplar DistancesT. Malisiewicz, A. A. Efros, Recognition by Association via Learning Per-exemplar Distances, CVPR 2008http://www.cs.cmu.edu/~tmalisie/projects/cvpr08/dfuns.tar.gzObject RecognitionBiologically motivated object recognitionT. Serre, L. Wolf and T. Poggio. Object recognition with features inspired by visual cortex, CVPR 2005http://cbcl.mit.edu/software-datasets/standardmodel/index.htmlObject SegmentationGeodesic Star Convexity for Interactive Image SegmentationV. Gulshan, C. Rother, A. Criminisi, A. Blake and A. Zisserman. Geodesic star convexity for interactive image segmentationhttp://www.robots.ox.ac.uk/~vgg/software/iseg/index.shtmlObject SegmentationClassCut for Unsupervised Class SegmentationB. Alexe, T. Deselaers and V. Ferrari, ClassCut for Unsupervised Class Segmentation, ECCV 2010http://www.vision.ee.ethz.ch/~calvin/classcut/ClassCut-release.zipObject SegmentationSparse to Dense LabelingP. Ochs, T. Brox, Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions, ICCV 2011http://lmb.informatik.uni-freiburg.de/resources/binaries/SparseToDenseLabeling.tar.gzOptical FlowOptical Flow by Deqing SunD. Sun, S. Roth, M. J. Black, Secrets of Optical Flow Estimation and Their Principles, CVPR, 2010http://www.cs.brown.edu/~dqsun/code/flow_code.zipOptical FlowClassical Variational Optical FlowT. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, ECCV 2004http://lmb.informatik.uni-freiburg.de/resources/binaries/Optical FlowLarge Displacement Optical FlowT. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, PAMI 2011http://lmb.informatik.uni-freiburg.de/resources/binaries/Optical FlowDense Point TrackingN. Sundaram, T. Brox, K. Keutzer Dense point trajectories by GPU-accelerated large displacement optical flow, ECCV 2010http://lmb.informatik.uni-freiburg.de/resources/binaries/Optical FlowOptical Flow EvaluationS. Baker et al. A Database and Evaluation Methodology for Optical Flow, IJCV, 2011http://vision.middlebury.edu/flow/Optical FlowHorn and Schunck's Optical Flow http://www.cs.brown.edu/~dqsun/code/hs.zipOptical FlowBlack and Anandan's Optical Flow http://www.cs.brown.edu/~dqsun/code/ba.zipPose EstimationTraining Deformable Models for LocalizationRamanan, D. "Learning to Parse Images of Articulated Bodies." NIPS 2006http://www.ics.uci.edu/~dramanan/papers/parse/index.htmlPose EstimationCalvin Upper-Body DetectorE. Marcin, F. Vittorio, Better Appearance Models for Pictorial Structures, BMVC 2009http://www.vision.ee.ethz.ch/~calvin/calvin_upperbody_detector/Pose EstimationArticulated Pose Estimation using Flexible Mixtures of PartsY. Yang, D. Ramanan, Articulated Pose Estimation using Flexible Mixtures of Parts, CVPR 2011http://phoenix.ics.uci.edu/software/pose/Pose EstimationEstimating Human Pose from Occluded ImagesJ.-B. Huang and M.-H. Yang, Estimating Human Pose from Occluded Images, ACCV 2009http://faculty.ucmerced.edu/mhyang/code/accv09_pose.zipSaliency DetectionSaliency detection: A spectral residual approachX. Hou and L. Zhang. Saliency detection: A spectral residual approach. CVPR, 2007http://www.klab.caltech.edu/~xhou/projects/spectralResidual/spectralresidual.htmlSaliency DetectionSaliency Using Natural statisticsL. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 2008http://cseweb.ucsd.edu/~l6zhang/Saliency DetectionAttention via Information MaximizationN. Bruce and J. Tsotsos. Saliency based on information maximization. In NIPS, 2005http://www.cse.yorku.ca/~neil/AIM.zipSaliency DetectionItti, Koch, and Niebur' saliency detectionL. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 1998http://www.saliencytoolbox.net/Saliency DetectionFrequency-tuned salient region detectionR. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009http://ivrgwww.epfl.ch/supplementary_material/RK_CVPR09/index.htmlSaliency DetectionSaliency-based video segmentationK. Fukuchi, K. Miyazato, A. Kimura, S. Takagi and J. Yamato, Saliency-based video segmentation with graph cuts and sequentially updated priors, ICME 2009http://www.brl.ntt.co.jp/people/akisato/saliency3.htmlSaliency DetectionSegmenting salient objects from images and videosE. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos. CVPR, 2010http://www.cse.oulu.fi/MVG/Downloads/saliencySaliency DetectionGraph-based visual saliencyJ. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007http://www.klab.caltech.edu/~harel/share/gbvs.phpSaliency DetectionLearning to Predict Where Humans LookT. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look, ICCV, 2009http://people.csail.mit.edu/tjudd/WherePeopleLook/index.htmlSaliency DetectionSpectrum Scale Space based Visual SaliencyJ Li, M D. Levine, X An and H. He, Saliency Detection Based on Frequency and Spatial Domain Analyses, BMVC 2011http://www.cim.mcgill.ca/~lijian/saliency.htmSaliency DetectionDiscriminant Saliency for Visual Recognition from Cluttered ScenesD. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes, NIPS, 2004http://www.svcl.ucsd.edu/projects/saliency/Saliency DetectionContext-aware saliency detectionS. 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.htmlSaliency DetectionSaliency detection using maximum symmetric surroundR. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 2010http://ivrg.epfl.ch/supplementary_material/RK_ICIP2010/index.htmlSaliency DetectionGlobal Contrast based Salient Region DetectionM.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection. CVPR, 2011http://cg.cs.tsinghua.edu.cn/people/~cmm/saliency/Saliency DetectionLearning Hierarchical Image Representation with Sparsity, Saliency and LocalityJ. Yang and M.-H. Yang, Learning Hierarchical Image Representation with Sparsity, Saliency and Locality, BMVC 2011 Sparse RepresentationCentralized Sparse Representation for Image RestorationW. Dong, L. Zhang and G. Shi, “Centralized Sparse Representation for Image Restoration,” ICCV 2011http://www4.comp.polyu.edu.hk/~cslzhang/code/CSR_IR.zipSparse RepresentationEfficient sparse coding algorithmsH. Lee, A. Battle, R. Rajat and A. Y. Ng, Efficient sparse coding algorithms, NIPS 2007http://ai.stanford.edu/~hllee/softwares/nips06-sparsecoding.htmSparse RepresentationFisher Discrimination Dictionary Learning for Sparse RepresentationM. Yang, L. Zhang, X. Feng and D. Zhang, Fisher Discrimination Dictionary Learning for Sparse Representation, ICCV 2011http://www4.comp.polyu.edu.hk/~cslzhang/code/FDDL.zipSparse RepresentationRobust Sparse Coding for Face RecognitionM. Yang, L. Zhang, J. Yang and D. Zhang, “Robust Sparse Coding for Face Recognition,” CVPR 2011http://www4.comp.polyu.edu.hk/~cslzhang/code/RSC.zipSparse RepresentationSparse and Redundant Representations: From Theory to Applications in Signal and Image ProcessingM. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processinghttp://www.cs.technion.ac.il/~elad/Various/Matlab-Package-Book.rarSparse RepresentationSPArse Modeling SoftwareJ. Mairal, F. Bach, J. Ponce and G. Sapiro. Online Learning for Matrix Factorization and Sparse Coding, JMLR 2010http://www.di.ens.fr/willow/SPAMS/Sparse RepresentationSparse coding simulation softwareOlshausen BA, Field DJ, "Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images", Nature 1996http://redwood.berkeley.edu/bruno/sparsenet/Sparse RepresentationA Linear Subspace Learning Approach via Sparse CodingL. Zhang, P. Zhu, Q. Hu and D. Zhang, “A Linear Subspace Learning Approach via Sparse Coding,” ICCV 2011http://www4.comp.polyu.edu.hk/~cslzhang/code/LSL_SC.zipStereoConstant-Space Belief PropagationQ. Yang, L. Wang, and N. Ahuja, A Constant-Space Belief Propagation Algorithm for Stereo Matching, CVPR 2010http://www.cs.cityu.edu.hk/~qiyang/publications/code/cvpr-10-csbp/csbp.htmStereoStereo EvaluationD. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV 2001http://vision.middlebury.edu/stereo/Image Denoising andStereo MatchingEfficient Belief Propagation for Early VisionP. F. Felzenszwalb and D. P. Huttenlocher, Efficient Belief Propagation for Early Vision, IJCV, 2006http://www.cs.brown.edu/~pff/bp/Structure from motionNonrigid Structure From Motion in Trajectory Space http://cvlab.lums.edu.pk/nrsfm/index.htmlStructure from motionlibmv http://code.google.com/p/libmv/Structure from motionBundlerN. Snavely, S M. Seitz, R Szeliski. Photo Tourism: Exploring image collections in 3D. SIGGRAPH 2006http://phototour.cs.washington.edu/bundler/Structure from motionFIT3D http://www.fit3d.info/Structure from motionVisualSFM : A Visual Structure from Motion System http://www.cs.washington.edu/homes/ccwu/vsfm/Structure from motionOpenSourcePhotogrammetry http://opensourcephotogrammetry.blogspot.com/Structure from motionStructure and Motion Toolkit in Matlab http://cms.brookes.ac.uk/staff/PhilipTorr/Code/code_page_4.htmStructure from motionStructure from Motion toolbox for Matlab by Vincent Rabaud http://code.google.com/p/vincents-structure-from-motion-matlab-toolbox/Subspace LearningGeneralized Principal Component AnalysisR. Vidal, Y. Ma and S. Sastry. Generalized Principal Component Analysis (GPCA), CVPR 2003http://www.vision.jhu.edu/downloads/main.php?dlID=c1Text RecognitionText recognition in the wildK. Wang, B. Babenko, and S. Belongie, End-to-end Scene Text Recognition, ICCV 2011http://vision.ucsd.edu/~kai/grocr/Text RecognitionNeocognitron for handwritten digit recognitionK. Fukushima: "Neocognitron for handwritten digit recognition", Neurocomputing, 2003http://visiome.neuroinf.jp/modules/xoonips/detail.php?item_id=375Texture SynthesisImage Quilting for Texture Synthesis and TransferA. A. Efros and W. T. Freeman, Image Quilting for Texture Synthesis and Transfer, SIGGRAPH 2001http://www.cs.cmu.edu/~efros/quilt_research_code.zipVisual TrackingGPU Implementation of Kanade-Lucas-Tomasi Feature TrackerS. N Sinha, J.-M. Frahm, M. Pollefeys and Y. Genc, Feature Tracking and Matching in Video Using Programmable Graphics Hardware, MVA, 2007http://cs.unc.edu/~ssinha/Research/GPU_KLT/Visual TrackingSuperpixel TrackingS. Wang, H. Lu, F. Yang, and M.-H. Yang, Superpixel Tracking, ICCV 2011http://faculty.ucmerced.edu/mhyang/papers/iccv11a.htmlVisual TrackingTracking with Online Multiple Instance LearningB. Babenko, M.-H. Yang, S. Belongie, Visual Tracking with Online Multiple Instance Learning, PAMI 2011http://vision.ucsd.edu/~bbabenko/project_miltrack.shtmlVisual TrackingMotion Tracking in Image SequencesC. Stauffer and W. E. L. Grimson. Learning patterns of activity using real-time tracking, PAMI, 2000http://www.cs.berkeley.edu/~flw/tracker/Visual TrackingL1 TrackingX. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009http://www.dabi.temple.edu/~hbling/code_data.htmVisual TrackingOnline Discriminative Object Tracking with Local Sparse RepresentationQ. Wang, F. Chen, W. Xu, and M.-H. Yang, Online Discriminative Object Tracking with Local Sparse Representation, WACV 2012http://faculty.ucmerced.edu/mhyang/code/wacv12a_code.zipVisual TrackingKLT: An Implementation of the Kanade-Lucas-Tomasi Feature TrackerB. D. Lucas and T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. IJCAI, 1981http://www.ces.clemson.edu/~stb/klt/Visual TrackingOnline boosting trackersH. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR, 2006http://www.vision.ee.ethz.ch/boostingTrackers/Visual TrackingVisual Tracking DecompositionJ Kwon and K. M. Lee, Visual Tracking Decomposition, CVPR 2010http://cv.snu.ac.kr/research/~vtd/Visual TrackingGlobally-Optimal Greedy Algorithms for Tracking a Variable Number of ObjectsH. Pirsiavash, D. Ramanan, C. Fowlkes. "Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects, CVPR 2011http://www.ics.uci.edu/~hpirsiav/papers/tracking_cvpr11_release_v1.0.tar.gzVisual TrackingLucas-Kanade affine template trackingS. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, IJCV 2002http://www.mathworks.com/matlabcentral/fileexchange/24677-lucas-kanade-affine-template-trackingVisual TrackingObject TrackingA. Yilmaz, O. Javed and M. Shah, Object Tracking: A Survey, ACM Journal of Computing Surveys, Vol. 38, No. 4, 2006http://plaza.ufl.edu/lvtaoran/object%20tracking.htmVisual TrackingVisual Tracking with Histograms and Articulating BlocksS. M. Shshed Nejhum, J. Ho, and M.-H.Yang, Visual Tracking with Histograms and Articulating Blocks, CVPR 2008http://www.cise.ufl.edu/~smshahed/tracking.htmVisual TrackingTracking using Pixel-Wise PosteriorsC. Bibby and I. Reid, Tracking using Pixel-Wise Posteriors, ECCV 2008http://www.robots.ox.ac.uk/~cbibby/research_pwp.shtmlVisual TrackingIncremental Learning for Robust Visual TrackingD. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007http://www.cs.toronto.edu/~dross/ivt/Visual TrackingParticle Filter Object Tracking http://blogs.oregonstate.edu/hess/code/particles/

计算机视觉算法的开源代码

Other useful links (dataset, lectures, and other softwares)