计算机视觉与模式识别 code

来源：互联网发布：2017广电禁止网络电视编辑：程序博客网时间：2024/05/18 22:11

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

TypeTopicNameReferenceLinkCodeStructure from motionlibmv http://code.google.com/p/libmv/CodeDimension ReductionLLE http://www.cs.nyu.edu/~roweis/lle/code.htmlCodeClusteringSpectral Clustering - UCSD Project http://vision.ucsd.edu/~sagarwal/spectral-0.2.tgzCodeClusteringK-Means 323个Item- Oxford Code http://www.cs.ucf.edu/~vision/Code/vggkmeans.zipCodeImage 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.htmCodeStructure from motionStructure from Motion toolbox for Matlab by Vincent Rabaud http://code.google.com/p/vincents-structure-from-motion-matlab-toolbox/CodeMultiple View GeometryMatlab Functions for Multiple View Geometry http://www.robots.ox.ac.uk/~vgg/hzbook/code/CodeObject 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/CodeImage 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.htmlCodeVisual TrackingTracking using Pixel-Wise PosteriorsC. Bibby and I. Reid, Tracking using Pixel-Wise Posteriors, ECCV 2008http://www.robots.ox.ac.uk/~cbibby/research_pwp.shtmlCodeVisual 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.htmCodeSparse 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.zipCodeFeature 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.tgzCodeDensity EstimationKernel Density Estimation Toolbox http://www.ics.uci.edu/~ihler/code/kde.htmlCodeIllumination, 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#shadowDetectionCodeImage Denoising,Image Super-resolution, andImage 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/CodeIllumination, 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#skyModelCodeVisual 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-trackingCodeSaliency 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.htmlCodeDimension ReductionLaplacian Eigenmaps http://www.cse.ohio-state.edu/~mbelkin/algorithms/Laplacian.tarCodeIllumination, 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#skyModelCodeImage 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.zipCodeImage 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/CodeVisual TrackingVisual Tracking DecompositionJ Kwon and K. M. Lee, Visual Tracking Decomposition, CVPR 2010http://cv.snu.ac.kr/research/~vtd/CodeVisual 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/CodeObject DetectionRecognition using regionsC. Gu, J. J. Lim, P. Arbelaez, and J. Malik, CVPR 2009http://www.cs.berkeley.edu/~chunhui/publications/cvpr09_v2.zipCodeSaliency 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/CodeImage 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.zipCodeCommon 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.gzCodeImage DenoisingBLS-GSM http://decsai.ugr.es/~javier/denoise/CodeCamera 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/CodeDepth SensorKinect SDKhttp://www.microsoft.com/en-us/kinectforwindows/http://www.microsoft.com/en-us/kinectforwindows/CodeImage 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.zipCodeImage DenoisingGaussian Field of Experts http://www.cs.huji.ac.il/~yweiss/BRFOE.zipCodeObject DetectionPoseletL. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009http://www.eecs.berkeley.edu/~lbourdev/poselets/CodeKernels 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.zipCodeNearest Neighbors MatchingSpectral HashingY. Weiss, A. Torralba, R. Fergus, Spectral Hashing, NIPS 2008http://www.cs.huji.ac.il/~yweiss/SpectralHashing/CodeImage DenoisingField of Experts http://www.cs.brown.edu/~roth/research/software.htmlCodeImage SegmentationMultiscale Segmentation TreeE. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,” ACCV 2009 andN. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996http://vision.ai.uiuc.edu/segmentationCodeMultiple Instance LearningMILISZ. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010 CodeNearest Neighbors MatchingFLANN: Fast Library for Approximate Nearest Neighbors http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANNCodeFeature 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/CodeAlpha MattingSpectral MattingA. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008http://www.vision.huji.ac.il/SpectralMatting/CodeMulti-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/CodeClusteringSelf-Tuning Spectral Clustering http://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.htmlCodeFeature 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.htmlCodeImage 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.htmlCodeMultiple Kernel LearningDOGMAF. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010http://dogma.sourceforge.net/CodeDistance Metric LearningMatlab Toolkit for Distance Metric Learning http://www.cs.cmu.edu/~liuy/distlearn.htmCodeOptical FlowBlack and Anandan's Optical Flow http://www.cs.brown.edu/~dqsun/code/ba.zipCodeText RecognitionText recognition in the wildK. Wang, B. Babenko, and S. Belongie, End-to-end Scene Text Recognition, ICCV 2011http://vision.ucsd.edu/~kai/grocr/CodeMRF 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/CodeSaliency 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.htmlCodeSaliency 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.htmlCodeStereoStereo EvaluationD. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV 2001http://vision.middlebury.edu/stereo/CodeImage SegmentationQuick-ShiftA. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking, ECCV, 2008http://www.vlfeat.org/overview/quickshift.htmlCodeSaliency DetectionGraph-based visual saliencyJ. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007http://www.klab.caltech.edu/~harel/share/gbvs.phpCodeClusteringK-Means - VLFeat http://www.vlfeat.org/CodeObject DetectionA simple object detector with boostingICCV 2005 short courses on Recognizing and Learning Object Categorieshttp://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.htmlCodeImage Quality AssessmentStructural SIMilarity https://ece.uwaterloo.ca/~z70wang/research/ssim/CodeStructure from motionFIT3D http://www.fit3d.info/CodeImage DenoisingBM3D http://www.cs.tut.fi/~foi/GCF-BM3D/CodeSaliency 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/CodeImage 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.zipCodeSaliency 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/CodeVisual 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/CodeSaliency 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/CodeFeature Detection,Feature Extraction, andAction 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.zipandhttp://www.nada.kth.se/cvap/abstracts/cvap284.htmlCodeTexture 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.zipCodeImage DenoisingNon-local Means http://dmi.uib.es/~abuades/codis/NLmeansfilter.mCodeLow-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.htmlCodeObject 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.gzCodeImage 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.zipCodeImage Quality AssessmentSPIQA http://vision.ai.uiuc.edu/~bghanem2/shared_code/SPIQA_code.zipCodeObject 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.htmlCodeIllumination, 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.htmlCodeIllumination, 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.zipCodeMRF 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.zipCodeOptical FlowOptical Flow EvaluationS. Baker et al. A Database and Evaluation Methodology for Optical Flow, IJCV, 2011http://vision.middlebury.edu/flow/CodeImage 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.htmlCodeMRF 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.zipCodeObject DetectionFeature CombinationP. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009http://www.vision.ee.ethz.ch/~pgehler/projects/iccv09/index.htmlCodeStructure from motionVisualSFM : A Visual Structure from Motion System http://www.cs.washington.edu/homes/ccwu/vsfm/CodeNearest Neighbors MatchingANN: Approximate Nearest Neighbor Searching http://www.cs.umd.edu/~mount/ANN/CodeSaliency 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 CodeOptical 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.zipCodeImage 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.zipCodeGraph 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/CodeObject 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.htmlCodeObject 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.htmlCodeDimension ReductionDiffusion maps http://www.stat.cmu.edu/~annlee/software.htmCodeMultiple Kernel LearningSHOGUNS. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning. JMLR, 2006http://www.shogun-toolbox.org/CodeDistance TransformationDistance Transforms of Sampled Functions http://people.cs.uchicago.edu/~pff/dt/CodeImage 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.zipCodeFeature ExtractionPCA-SIFTY. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004http://www.cs.cmu.edu/~yke/pcasift/CodeVisual TrackingParticle Filter Object Tracking http://blogs.oregonstate.edu/hess/code/particles/CodeFeature 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#CodeMultiple 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/CodeAction 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_trajectoriesCodeImage SegmentationEfficient Graph-based Image Segmentation - C++ codeP. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004http://people.cs.uchicago.edu/~pff/segment/CodeObject ProposalParametric min-cutJ. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010http://sminchisescu.ins.uni-bonn.de/code/cpmc/CodeCommon 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=0CodeSparse 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/CodeMRF 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.zipCodeOptical FlowHorn and Schunck's Optical Flow http://www.cs.brown.edu/~dqsun/code/hs.zipCodeSparse 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.rarCodeImage 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.htmlCodeCamera CalibrationCamera Calibration Toolbox for Matlabhttp://www.vision.caltech.edu/bouguetj/calib_doc/htmls/ref.htmlhttp://www.vision.caltech.edu/bouguetj/calib_doc/CodeImage 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.htmlCodeFeature DetectionEdge Foci Interest PointsL. Zitnickand K. Ramnath, Edge Foci Interest Points, ICCV, 2011http://research.microsoft.com/en-us/um/people/larryz/edgefoci/edge_foci.htmCodeFeature 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/CodeSubspace 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=c1CodeCamera CalibrationEasyCamCalibJ. Barreto, J. Roquette, P. Sturm, and F. Fonseca, Automatic camera calibration applied to medical endoscopy, BMVC, 2009http://arthronav.isr.uc.pt/easycamcalib/CodeImage SegmentationSuperpixel by Gerg MoriX. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003http://www.cs.sfu.ca/~mori/research/superpixels/CodeImage 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.htmlCodeSaliency 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.htmCodeSparse 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.zipCodeObject 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/CodeObject 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.gzCodeOptical 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/CodeVisual 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.shtmlCodeGraph 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/CodeMachine 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/CodeImage 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.htmCodeObject 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/CodeMultiple Instance LearningMIForestsC. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010http://www.ymer.org/amir/software/milforests/CodeOptical 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/CodeMultiple 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.htmlCodeImage 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-malikCodeFeature 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/CodeLow-Rank ModelingLow-Rank Matrix Recovery and Completion http://perception.csl.uiuc.edu/matrix-rank/sample_code.htmlCodeObject DetectionA simple parts and structure object detectorICCV 2005 short courses on Recognizing and Learning Object Categorieshttp://people.csail.mit.edu/fergus/iccv2005/partsstructure.htmlCodeKernels and DistancesDiffusion-based distanceH. Ling and K. Okada, Diffusion Distance for Histogram Comparison, CVPR 2006http://www.dabi.temple.edu/~hbling/code/DD_v1.zipCodeImage DenoisingK-SVD http://www.cs.technion.ac.il/~ronrubin/Software/ksvdbox13.zipCodeMultiple Kernel LearningSimpleMKLA. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl. JMRL, 2008http://asi.insa-rouen.fr/enseignants/~arakotom/code/mklindex.htmlCodeFeature 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.zipCodeSparse 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.htmCodeMulti-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/CodeMulti-View StereoMulti-View Stereo EvaluationS. Seitz et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006http://vision.middlebury.edu/mview/CodeStructure from motionStructure and Motion Toolkit in Matlab http://cms.brookes.ac.uk/staff/PhilipTorr/Code/code_page_4.htmCodePose EstimationTraining Deformable Models for LocalizationRamanan, D. "Learning to Parse Images of Articulated Bodies." NIPS 2006http://www.ics.uci.edu/~dramanan/papers/parse/index.htmlCodeLow-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.htmlCodeDimension ReductionISOMAP http://isomap.stanford.edu/CodeAlpha MattingLearning-based MattingY. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009http://www.mathworks.com/matlabcentral/fileexchange/31412CodeImage SegmentationNormalized CutJ. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000http://www.cis.upenn.edu/~jshi/software/CodeImage 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/CodeSparse 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.zipCodeText RecognitionNeocognitron for handwritten digit recognitionK. Fukushima: "Neocognitron for handwritten digit recognition", Neurocomputing, 2003http://visiome.neuroinf.jp/modules/xoonips/detail.php?item_id=375CodeImage 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.htmCodeNearest 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.phpCodeObject 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.zipCodeImage Quality AssessmentFeature SIMilarity Index http://www4.comp.polyu.edu.hk/~cslzhang/IQA/FSIM/FSIM.htmCodeSaliency DetectionAttention via Information MaximizationN. Bruce and J. Tsotsos. Saliency based on information maximization. In NIPS, 2005http://www.cse.yorku.ca/~neil/AIM.zipCodeImage DenoisingWhat makes a good model of natural images ?Y. Weiss and W. T. Freeman, CVPR 2007http://www.cs.huji.ac.il/~yweiss/BRFOE.zipCodeImage 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.gzCodeObject 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.shtmlCodeFeature 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/CodeMRF 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.zipCodeFeature 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/CodeVisual 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/CodeFeature 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/CodeImage 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.htmlCodeImage 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.htmlCodeFeature 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.htmCodeAlpha 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.htmlCodeImage 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.zipCodePose 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/CodeFeature 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/CodeFeature ExtractionGlobal and Efficient Self-SimilarityT. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection. CVPR 2010andT. 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.tgzCodeImage 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.htmlCodeFeature 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/CodeImage 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/CodeObject 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.gzCodeVisual TrackingSuperpixel TrackingS. Wang, H. Lu, F. Yang, and M.-H. Yang, Superpixel Tracking, ICCV 2011http://faculty.ucmerced.edu/mhyang/papers/iccv11a.htmlCodeSparse 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/CodeSaliency 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.htmlCodeImage FilteringGuided Image FilteringK. He, J. Sun, X. Tang, Guided Image Filtering, ECCV 2010http://personal.ie.cuhk.edu.hk/~hkm007/eccv10/guided-filter-code-v1.rarCodeKernels and DistancesFast Directional Chamfer Matching http://www.umiacs.umd.edu/~mingyliu/src/fdcm_matlab_wrapper_v0.2.zipCodeVisual TrackingL1 TrackingX. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009http://www.dabi.temple.edu/~hbling/code_data.htmCodeObject ProposalRegion-based Object ProposalI. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010http://vision.cs.uiuc.edu/proposals/CodeObject 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/CodeDimension ReductionDimensionality Reduction Toolbox http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.htmlCodeObject DetectionViola-Jones Object DetectionP. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR, 2001http://pr.willowgarage.com/wiki/FaceDetectionCodeObject 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.htmlCodeSaliency 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.htmlCodeImage 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/CodeMachine LearningFastICA package for MATLABhttp://research.ics.tkk.fi/ica/book/http://research.ics.tkk.fi/ica/fastica/CodeFeature 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/CodeStructure from motionBundlerN. Snavely, S M. Seitz, R Szeliski. Photo Tourism: Exploring image collections in 3D. SIGGRAPH 2006http://phototour.cs.washington.edu/bundler/CodeVisual 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.zipCodeAlpha 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.gzCodeImage 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/CodeVisual 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/CodeFeature 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/CodeImage 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.zipCodeImage 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.zipCodeMultiple Kernel LearningOpenKernel.orgF. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011http://www.openkernel.org/CodeImage 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-segmentationCodeImage SegmentationBiased Normalized CutS. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut, CVPR 2011http://www.cs.berkeley.edu/~smaji/projects/biasedNcuts/CodeStereoConstant-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.htmCodeFeature 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.htmlCodeVisual TrackingOnline boosting trackersH. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR, 2006http://www.vision.ee.ethz.ch/boostingTrackers/CodeImage 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.htmlCodeFeature Detection andFeature ExtractionScale-invariant feature transform (SIFT) - VLFeatD. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.http://www.vlfeat.org/CodeClusteringSpectral Clustering - UW Project http://www.stat.washington.edu/spectral/CodeImage DeblurringAnalyzing spatially varying blurA. Chakrabarti, T. Zickler, and W. T. Freeman, Analyzing Spatially-varying Blur, CVPR 2010http://www.eecs.harvard.edu/~ayanc/svblur/CodeMultiple Instance LearningDD-SVMYixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 2004 CodeFeature 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/CodeImage 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.htmlCodeStructure from motionNonrigid Structure From Motion in Trajectory Space http://cvlab.lums.edu.pk/nrsfm/index.htmlCodeAlpha MattingShared MattingE. S. L. Gastal and M. M. Oliveira, Computer Graphics Forum, 2010http://www.inf.ufrgs.br/~eslgastal/SharedMatting/CodeAction Recognition3D Gradients (HOG3D)A. Klaser, M. Marszałek, and C. Schmid, BMVC, 2008.http://lear.inrialpes.fr/people/klaeser/research_hog3dCodeImage DenoisingKernel Regressions http://www.soe.ucsc.edu/~htakeda/MatlabApp/KernelRegressionBasedImageProcessingToolBox_ver1-1beta.zipCodeFeature DetectionBoundary Preserving Dense Local RegionsJ. Kim and K. Grauman, Boundary Preserving Dense Local Regions, CVPR 2011http://vision.cs.utexas.edu/projects/bplr/bplr.htmlCodeImage UnderstandingSuperParsingJ. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image Parsing with Superpixels, ECCV 2010http://www.cs.unc.edu/~jtighe/Papers/ECCV10/eccv10-jtighe-code.zipCodeImage 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/CodeImage 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.zipCodeImage 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/#downloadsCodeFeature 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.htmlCodeImage 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.htmlCodeIllumination, 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#skyModelCodePose EstimationCalvin Upper-Body DetectorE. Marcin, F. Vittorio, Better Appearance Models for Pictorial Structures, BMVC 2009http://www.vision.ee.ethz.ch/~calvin/calvin_upperbody_detector/CodeImage 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.htmCodeFeature 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.phpCodePose 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.zipCodeStructure from motionOpenSourcePhotogrammetry http://opensourcephotogrammetry.blogspot.com/CodeImage 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.zipCodeNearest Neighbors MatchingCoherency Sensitive HashingS. Korman, S. Avidan, Coherency Sensitive Hashing, ICCV 2011http://www.eng.tau.ac.il/~simonk/CSH/index.htmlCodeImage 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/CodeSaliency 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.htmlCodeMRF 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.zipCodeFeature DetectionCanny Edge DetectionJ. Canny, A Computational Approach To Edge Detection, PAMI, 1986http://www.mathworks.com/help/toolbox/images/ref/edge.htmlCodeObject 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/CodeImage 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.htmlCodeImage Quality AssessmentDegradation Model http://users.ece.utexas.edu/~bevans/papers/2000/imageQuality/index.htmlCodeObject 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/CodeImage 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.zipCodeImage 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.zipCodeFeature DetectionFAST Corner DetectionE. Rosten and T. Drummond, Machine learning for high-speed corner detection, ECCV, 2006http://www.edwardrosten.com/work/fast.htmlCodeImage 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.htmlCodeFeature 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/softwareCodeVisual 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.gzCodeSaliency 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/saliencyCodeVisual 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.htmCodeMachine LearningBoosting Resources by Liangliang Caohttp://www.ifp.illinois.edu/~cao4/reading/boostingbib.htmhttp://www.ifp.illinois.edu/~cao4/reading/boostingbib.htmCodeMachine LearningNetlab Neural Network SoftwareC. M. Bishop, Neural Networks for Pattern RecognitionㄝOxford University Press, 1995http://www1.aston.ac.uk/eas/research/groups/ncrg/resources/netlab/CodeOptical 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/CodeSparse 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.zipCourseComputer VisionIntroduction to Computer Vision, Stanford University, Winter 2010-2011Fei-Fei Lihttp://vision.stanford.edu/teaching/cs223b/CourseComputer VisionComputer Vision: From 3D Reconstruction to Visual Recognition, Fall 2012Silvio Savarese and Fei-Fei Lihttps://www.coursera.org/course/computervisionCourseComputer VisionComputer Vision, University of Texas at Austin, Spring 2011Kristen Graumanhttp://www.cs.utexas.edu/~grauman/courses/spring2011/index.htmlCourseComputer VisionLearning-Based Methods in Vision, CMU, Spring 2012Alexei “Alyosha” Efros and Leonid Sigalhttps://docs.google.com/document/pub?id=1jGBn7zPDEaU33fJwi3YI_usWS-U6gpSSJotV_2gDrL0CourseVisual RecognitionVisual Recognition, University of Texas at Austin, Fall 2011Kristen Graumanhttp://www.cs.utexas.edu/~grauman/courses/fall2011/schedule.htmlCourseComputer VisionIntroduction to Computer VisionJames Hays, Brown University, Fall 2011http://www.cs.brown.edu/courses/cs143/CourseComputer VisionComputer Vision, University of North Carolina at Chapel Hill, Spring 2010Svetlana Lazebnikhttp://www.cs.unc.edu/~lazebnik/spring10/CourseComputer VisionComputer Vision: The Fundamentals, University of California at Berkeley, Fall 2012Jitendra Malikhttps://www.coursera.org/course/visionCourseComputational PhotographyComputational Photography, University of Illinois, Urbana-Champaign, Fall 2011Derek Hoiemhttp://www.cs.illinois.edu/class/fa11/cs498dh/CourseGraphical ModelsInference in Graphical Models, Stanford University, Spring 2012Andrea Montanari, Stanford Universityhttp://www.stanford.edu/~montanar/TEACHING/Stat375/stat375.htmlCourseComputer VisionComputer Vision, New York University, Fall 2012Rob Fergushttp://cs.nyu.edu/~fergus/teaching/vision_2012/index.htmlCourseComputer VisionAdvances in Computer VisionAntonio Torralba, MIT, Spring 2010http://groups.csail.mit.edu/vision/courses/6.869/CourseComputer VisionComputer Vision, University of Illinois, Urbana-Champaign, Spring 2012Derek Hoiemhttp://www.cs.illinois.edu/class/sp12/cs543/CourseComputational PhotographyComputational Photography, CMU, Fall 2011Alexei “Alyosha” Efroshttp://graphics.cs.cmu.edu/courses/15-463/2011_fall/463.htmlCourseComputer VisionComputer Vision, University of Washington, Winter 2012Steven Seitzhttp://www.cs.washington.edu/education/courses/cse455/12wi/LinkSource codeSource Code Collection for Reproducible Researchcollected by Xin Li, Lane Dept of CSEE, West Virginia Universityhttp://www.csee.wvu.edu/~xinl/reproducible_research.htmlLinkComputer VisionComputer Image Analysis, Computer Vision ConferencesUSChttp://iris.usc.edu/information/Iris-Conferences.htmlLinkComputer VisionCV Papers on the webCVPapershttp://www.cvpapers.com/index.htmlLinkComputer VisionCVonlineCVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Visionhttp://homepages.inf.ed.ac.uk/rbf/CVonline/LinkDatasetCompiled list of recognition datasetscompiled by Kristen Graumanhttp://www.cs.utexas.edu/~grauman/courses/spring2008/datasets.htmLinkComputer VisionAnnotated Computer Vision Bibliographycompiled by Keith Pricehttp://iris.usc.edu/Vision-Notes/bibliography/contents.htmlLinkComputer VisionThe Computer Vision homepage http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.htmlLinkComputer Vision IndustryThe Computer Vision IndustryDavid Lowehttp://www.cs.ubc.ca/~lowe/vision.htmlLinkSource codeComputer Vision Algorithm ImplementationsCVPapershttp://www.cvpapers.com/rr.htmlLinkComputer VisionCV Datasets on the webCVPapershttp://www.cvpapers.com/datasets.htmlTalkVisual RecognitionUnderstanding Visual ScenesAntonio Torralba, MIThttp://videolectures.net/nips09_torralba_uvs/TalkNeuroscienceLearning in Hierarchical Architectures: from Neuroscience to Derived KernelsTomaso A. Poggio, McGovern Institute for Brain Research, Massachusetts Institute of Technologyhttp://videolectures.net/mlss09us_poggio_lhandk/TalkDeep LearningA tutorial on Deep LearningGeoffrey E. Hinton, Department of Computer Science, University of Torontohttp://videolectures.net/jul09_hinton_deeplearn/TalkBoostingTheory and Applications of BoostingRobert Schapire, Department of Computer Science, Princeton Universityhttp://videolectures.net/mlss09us_schapire_tab/TalkGraphical ModelsGraphical Models and message-passing algorithmsMartin J. Wainwright, University of California at Berkeleyhttp://videolectures.net/mlss2011_wainwright_messagepassing/TalkStatistical Learning TheoryStatistical Learning TheoryJohn Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College Londonhttp://videolectures.net/mlss04_taylor_slt/TalkGaussian ProcessGaussian Process BasicsDavid MacKay, University of Cambridgehttp://videolectures.net/gpip06_mackay_gpb/TalkInformation TheoryInformation TheoryDavid MacKay, University of Cambridgehttp://videolectures.net/mlss09uk_mackay_it/TalkOptimizationOptimization Algorithms in Machine LearningStephen J. Wright, Computer Sciences Department, University of Wisconsin - Madisonhttp://videolectures.net/nips2010_wright_oaml/TalkBayesian InferenceIntroduction To Bayesian InferenceChristopher Bishop, Microsoft Researchhttp://videolectures.net/mlss09uk_bishop_ibi/TalkBayesian NonparametricsModern Bayesian NonparametricsPeter Orbanz and Yee Whye Tehhttp://www.youtube.com/watch?v=F0_ih7THV94&feature=relmfuTalkKernels and DistancesMachine learning and kernel methods for computer visionFrancis R. Bach, INRIAhttp://videolectures.net/etvc08_bach_mlakm/TalkOptimizationConvex OptimizationLieven Vandenberghe, Electrical Engineering Department, University of California, Los Angeleshttp://videolectures.net/mlss2011_vandenberghe_convex/TalkOptimizationEnergy Minimization with Label costs and Applications in Multi-Model FittingYuri Boykov, Department of Computer Science, University of Western Ontariohttp://videolectures.net/nipsworkshops2010_boykov_eml/TalkObject DetectionObject Recognition with Deformable ModelsPedro Felzenszwalb, Brown Universityhttp://www.youtube.com/watch?v=_J_clwqQ4gITalkLow-level visionLearning and Inference in Low-Level VisionYair Weiss, School of Computer Science and Engineering, The Hebrew University of Jerusalemhttp://videolectures.net/nips09_weiss_lil/Talk3D Computer Vision3D Computer Vision: Past, Present, and FutureSteven Seitz, University of Washington, Google Tech Talk, 2011http://www.youtube.com/watch?v=kyIzMr917RcTalkOptimizationWho is Afraid of Non-Convex Loss Functions?Yann LeCun, New York Universityhttp://videolectures.net/eml07_lecun_wia/TalkSparse RepresentationSparse Methods for Machine Learning: Theory and AlgorithmsFrancis R. Bach, INRIAhttp://videolectures.net/nips09_bach_smm/TalkOptimization and Support Vector MachinesOptimization Algorithms in Support Vector MachinesStephen J. Wright, Computer Sciences Department, University of Wisconsin - Madisonhttp://videolectures.net/mlss09us_wright_oasvm/TalkInformation TheoryInformation Theory in Learning and ControlNaftali (Tali) Tishby, The Hebrew Universityhttp://www.youtube.com/watch?v=GKm53xGbAOk&feature=relmfuTalkRelative EntropyRelative EntropySergio Verdu, Princeton Universityhttp://videolectures.net/nips09_verdu_re/TutorialObject DetectionGeometry constrained parts based detectionSimon Lucey, Jason Saragih, ICCV 2011 Tutorialhttp://ci2cv.net/tutorials/iccv-2011/TutorialGraphical ModelsLearning with inference for discrete graphical modelsNikos Komodakis, Pawan Kumar, Nikos Paragios, Ramin Zabih, ICCV 2011 Tutorialhttp://www.csd.uoc.gr/~komod/ICCV2011_tutorial/TutorialVariational CalculusVariational methods for computer visionDaniel Cremers, Bastian Goldlucke, Thomas Pock, ICCV 2011 Tutorialhttp://cvpr.in.tum.de/tutorials/iccv2011Tutorial3D perceptionComputer Vision and 3D Perception for RoboticsRadu Bogdan Rusu, Gary Bradski, Caroline Pantofaru, Stefan Hinterstoisser, Stefan Holzer, Kurt Konolige and Andrea Vedaldi, ECCV 2010 Tutorialhttp://www.willowgarage.com/workshops/2010/eccvTutorialAction RecognitionLooking at people: The past, the present and the futureL. Sigal, T. Moeslund, A. Hilton, V. Kruger, ICCV 2011 Tutorialhttp://www.cs.brown.edu/~ls/iccv2011tutorial.htmlTutorialNon-linear Least SquaresComputer vision fundamentals: robust non-linear least-squares and their applicationsPascal Fua, Vincent Lepetit, ICCV 2011 Tutorialhttp://cvlab.epfl.ch/~fua/courses/lsq/TutorialAction RecognitionFrontiers of Human Activity AnalysisJ. K. Aggarwal, Michael S. Ryoo, and Kris Kitani, CVPR 2011 Tutorialhttp://cvrc.ece.utexas.edu/mryoo/cvpr2011tutorial/TutorialStructured PredictionStructured Prediction and Learning in Computer VisionS. Nowozin and C. Lampert, CVPR 2011 Tutorialhttp://www.nowozin.net/sebastian/cvpr2011tutorial/TutorialAction RecognitionStatistical and Structural Recognition of Human ActionsIvan Laptev and Greg Mori, ECCV 2010 Tutorialhttps://sites.google.com/site/humanactionstutorialeccv10/TutorialComputational SymmetryComputational Symmetry: Past, Current, FutureYanxi Liu, ECCV 2010 Tutorialhttp://vision.cse.psu.edu/research/symmComp/index.shtmlTutorialMatlabMatlab TutorialDavid Kriegman and Serge Belongiehttp://www.cs.unc.edu/~lazebnik/spring10/matlab.intro.htmlTutorialMatlabWriting Fast MATLAB CodePascal Getreuer, Yale Universityhttp://www.mathworks.com/matlabcentral/fileexchange/5685TutorialSpectral ClusteringA Tutorial on Spectral ClusteringUlrike von Luxburg, Max Planck Institute for Biological Cyberneticshttp://web.mit.edu/~wingated/www/introductions/tutorial_on_spectral_clustering.pdfTutorialFeature Learning, Image ClassificationFeature Learning for Image ClassificationKai Yu and Andrew Ng, ECCV 2010 Tutorialhttp://ufldl.stanford.edu/eccv10-tutorial/TutorialShape Analysis, Diffusion GeometryDiffusion Geometry Methods in Shape AnalysisA. Brontein and M. Bronstein, ECCV 2010 Tutorialhttp://tosca.cs.technion.ac.il/book/course_eccv10.htmlTutorialGraphical ModelsGraphical Models, Exponential Families, and Variational InferenceMartin J. Wainwright and Michael I. Jordan, University of California at Berkeleyhttp://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdfTutorialColor Image ProcessingColor image understanding: from acquisition to high-level image understandingTheo Gevers, Keigo Hirakawa, Joost van de Weijer, ICCV 2011 Tutorialhttp://www.cat.uab.cat/~joost/tutorial_iccv.htmlTutorialStructure from motionNonrigid Structure from MotionY. Sheikh and Sohaib Khan, ECCV 2010 Tutorialhttp://www.cs.cmu.edu/~yaser/ECCV2010Tutorial.htmlTutorialExpectation MaximizationA Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov ModelsJeff A. Bilmes, University of California at Berkeleyhttp://crow.ee.washington.edu/people/bulyko/papers/em.pdfTutorialDecision ForestsDecision forests for classification, regression, clustering and density estimationA. Criminisi, J. Shotton and E. Konukoglu, ICCV 2011 Tutorialhttp://research.microsoft.com/en-us/groups/vision/decisionforests.aspxTutorial3D point cloud processing3D point cloud processing: PCL (Point Cloud Library)R. Rusu, S. Holzer, M. Dixon, V. Rabaud, ICCV 2011 Tutorialhttp://www.pointclouds.org/media/iccv2011.htmlTutorialImage RegistrationTools and Methods for Image RegistrationBrown, G. Carneiro, A. A. Farag, E. Hancock, A. A. Goshtasby (Organizer), J. Matas, J.M. Morel, N. S. Netanyahu, F. Sur, and G. Yu, CVPR 2011 Tutorialhttp://www.imgfsr.com/CVPR2011/Tutorial6/TutorialNon-rigid registrationNon-rigid registration and reconstructionAlessio Del Bue, Lourdes Agapito, Adrien Bartoli, ICCV 2011 Tutorialhttp://www.isr.ist.utl.pt/~adb/tutorial/TutorialVariational CalculusVariational Methods in Computer VisionD. Cremers, B. Goldlücke, T. Pock, ECCV 2010 Tutorialhttp://cvpr.cs.tum.edu/tutorials/eccv2010TutorialDistance Metric LearningDistance Functions and Metric LearningM. Werman, O. Pele and B. Kulis, ECCV 2010 Tutorialhttp://www.cs.huji.ac.il/~ofirpele/DFML_ECCV2010_tutorial/TutorialFeature ExtractionImage and Video Description with Local Binary Pattern VariantsM. Pietikainen and J. Heikkila, CVPR 2011 Tutorialhttp://www.ee.oulu.fi/research/imag/mvg/files/pdf/CVPR-tutorial-final.pdfTutorialGame TheoryGame Theory in Computer Vision and Pattern RecognitionMarcello Pelillo and Andrea Torsello, CVPR 2011 Tutorialhttp://www.dsi.unive.it/~atorsell/cvpr2011tutorial/TutorialComputational ImagingFcam: an architecture and API for computational camerasKari Pulli, Andrew Adams, Timo Ahonen, Marius Tico, ICCV 2011 Tutorialhttp://fcam.garage.maemo.org/iccv2011.html

计算机视觉与模式识别 code

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

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