CV CODE

一、特征提取Feature Extraction:
   SIFT [1] [Demo program][SIFT Library] [VLFeat]
   PCA-SIFT [2] [Project]
   Affine-SIFT [3] [Project]
   SURF [4] [OpenSURF] [Matlab Wrapper]
   Affine Covariant Features [5] [Oxford project]
   MSER [6] [Oxford project] [VLFeat]
   Geometric Blur [7] [Code]
   Local Self-Similarity Descriptor [8] [Oxford implementation]
   Global and Efficient Self-Similarity [9] [Code]
   Histogram of Oriented Graidents [10] [INRIA Object Localization Toolkit] [OLT toolkit for Windows]
   GIST [11] [Project]
   Shape Context [12] [Project]
   Color Descriptor [13] [Project]
   Pyramids of Histograms of Oriented Gradients [Code]
   Space-Time Interest Points (STIP) [14][Project] [Code]
   Boundary Preserving Dense Local Regions [15][Project]
   Weighted Histogram[Code]
   Histogram-based Interest Points Detectors[Paper][Code]
   An OpenCV - C++ implementation of Local Self Similarity Descriptors [Project]
   Fast Sparse Representation with Prototypes[Project]
   Corner Detection [Project]
   AGAST Corner Detector: faster than FAST and even FAST-ER[Project]
二、图像分割Image Segmentation:
     Normalized Cut [1] [Matlab code]
     Gerg Mori’ Superpixel code [2] [Matlab code]
     Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]
     Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper]
     OWT-UCM Hierarchical Segmentation [5] [Resources]
     Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code]
     Quick-Shift [7] [VLFeat]
     SLIC Superpixels [8] [Project]
     Segmentation by Minimum Code Length [9] [Project]
     Biased Normalized Cut [10] [Project]
     Segmentation Tree [11-12] [Project]
     Entropy Rate Superpixel Segmentation [13] [Code]
     Fast Approximate Energy Minimization via Graph Cuts[Paper][Code]
     Efficient Planar Graph Cuts with Applications in Computer Vision[Paper][Code]
     Isoperimetric Graph Partitioning for Image Segmentation[Paper][Code]
     Random Walks for Image Segmentation[Paper][Code]
     Blossom V: A new implementation of a minimum cost perfect matching algorithm[Code]
     An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision[Paper][Code]
     Geodesic Star Convexity for Interactive Image Segmentation[Project]
     Contour Detection and Image Segmentation Resources[Project][Code]
     Biased Normalized Cuts[Project]
     Max-flow/min-cut[Project]
     Chan-Vese Segmentation using Level Set[Project]
     A Toolbox of Level Set Methods[Project]
     Re-initialization Free Level Set Evolution via Reaction Diffusion[Project]
     Improved C-V active contour model[Paper][Code]
     A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction[Paper][Code]
    Level Set Method Research by Chunming Li[Project]
三、目标检测Object Detection:
     A simple object detector with boosting [Project]
     INRIA Object Detection and Localization Toolkit [1] [Project]
     Discriminatively Trained Deformable Part Models [2] [Project]
     Cascade Object Detection with Deformable Part Models [3] [Project]
     Poselet [4] [Project]
     Implicit Shape Model [5] [Project]
     Viola and Jones’s Face Detection [6] [Project]
     Bayesian Modelling of Dyanmic Scenes for Object Detection[Paper][Code]
     Hand detection using multiple proposals[Project]
     Color Constancy, Intrinsic Images, and Shape Estimation[Paper][Code]
     Discriminatively trained deformable part models[Project]
     Gradient Response Maps for Real-Time Detection of Texture-Less Objects: LineMOD [Project]
     Image Processing On Line[Project]
     Robust Optical Flow Estimation[Project]
     Where's Waldo: Matching People in Images of Crowds[Project]
四、显著性检测Saliency Detection:
     Itti, Koch, and Niebur’ saliency detection [1] [Matlab code]
     Frequency-tuned salient region detection [2] [Project]
     Saliency detection using maximum symmetric surround [3] [Project]
     Attention via Information Maximization [4] [Matlab code]
     Context-aware saliency detection [5] [Matlab code]
     Graph-based visual saliency [6] [Matlab code]
     Saliency detection: A spectral residual approach. [7] [Matlab code]
     Segmenting salient objects from images and videos. [8] [Matlab code]
     Saliency Using Natural statistics. [9] [Matlab code]
     Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] [Code]
     Learning to Predict Where Humans Look [11] [Project]
     Global Contrast based Salient Region Detection [12] [Project]
     Bayesian Saliency via Low and Mid Level Cues[Project]
     Top-Down Visual Saliency via Joint CRF and Dictionary Learning[Paper][Code]
五、图像分类、聚类Image Classification, Clustering
     Pyramid Match [1] [Project]
     Spatial Pyramid Matching [2] [Code]
     Locality-constrained Linear Coding [3] [Project] [Matlab code]
     Sparse Coding [4] [Project] [Matlab code]
     Texture Classification [5] [Project]
     Multiple Kernels for Image Classification [6] [Project]
     Feature Combination [7] [Project]
     SuperParsing [Code]
     Large Scale Correlation Clustering Optimization[Matlab code]
     Detecting and Sketching the Common[Project]
     Self-Tuning Spectral Clustering[Project][Code]
     User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior[Paper][Code]
     Filters for Texture Classification[Project]
     Multiple Kernel Learning for Image Classification[Project]
    SLIC Superpixels[Project]
六、抠图Image Matting
     A Closed Form Solution to Natural Image Matting [Code]
     Spectral Matting [Project]
     Learning-based Matting [Code]
七、目标跟踪Object Tracking:
     A Forest of Sensors - Tracking Adaptive Background Mixture Models [Project]
     Object Tracking via Partial Least Squares Analysis[Paper][Code]
     Robust Object Tracking with Online Multiple Instance Learning[Paper][Code]
     Online Visual Tracking with Histograms and Articulating Blocks[Project]
     Incremental Learning for Robust Visual Tracking[Project]
     Real-time Compressive Tracking[Project]
     Robust Object Tracking via Sparsity-based Collaborative Model[Project]
     Visual Tracking via Adaptive Structural Local Sparse Appearance Model[Project]
     Online Discriminative Object Tracking with Local Sparse Representation[Paper][Code]
     Superpixel Tracking[Project]
     Learning Hierarchical Image Representation with Sparsity, Saliency and Locality[Paper][Code]
     Online Multiple Support Instance Tracking [Paper][Code]
     Visual Tracking with Online Multiple Instance Learning[Project]
     Object detection and recognition[Project]
     Compressive Sensing Resources[Project]
     Robust Real-Time Visual Tracking using Pixel-Wise Posteriors[Project]
     Tracking-Learning-Detection[Project][OpenTLD/C++ Code]
     the HandVu:vision-based hand gesture interface[Project]
八、Kinect:
     Kinect toolbox[Project]
     OpenNI[Project]
     zouxy09 CSDN Blog[Resource]
九、3D相关:
     3D Reconstruction of a Moving Object[Paper] [Code]
     Shape From Shading Using Linear Approximation[Code]
     Combining Shape from Shading and Stereo Depth Maps[Project][Code]
     Shape from Shading: A Survey[Paper][Code]
     A Spatio-Temporal Descriptor based on 3D Gradients (HOG3D)[Project][Code]
     Multi-camera Scene Reconstruction via Graph Cuts[Paper][Code]
     A Fast Marching Formulation of Perspective Shape from Shading under Frontal Illumination[Paper][Code]
     Reconstruction:3D Shape, Illumination, Shading, Reflectance, Texture[Project]
     Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers[Code]
     Learning 3-D Scene Structure from a Single Still Image[Project]
十、机器学习算法:
     Matlab class for computing Approximate Nearest Nieghbor (ANN) [Matlab class providing interface toANN library]
     Random Sampling[code]
     Probabilistic Latent Semantic Analysis (pLSA)[Code]
     FASTANN and FASTCLUSTER for approximate k-means (AKM)[Project]
     Fast Intersection / Additive Kernel SVMs[Project]
     SVM[Code]
     Ensemble learning[Project]
     Deep Learning[Net]
     Deep Learning Methods for Vision[Project]
     Neural Network for Recognition of Handwritten Digits[Project]
     Training a deep autoencoder or a classifier on MNIST digits[Project]
    THE MNIST DATABASE of handwritten digits[Project]
    Ersatz:deep neural networks in the cloud[Project]
    Deep Learning [Project]
    sparseLM : Sparse Levenberg-Marquardt nonlinear least squares in C/C++[Project]
    Weka 3: Data Mining Software in Java[Project]
    Invited talk "A Tutorial on Deep Learning" by Dr. Kai Yu (余凯)[Video]
    CNN - Convolutional neural network class[Matlab Tool]
    Yann LeCun's Publications[Wedsite]
    LeNet-5, convolutional neural networks[Project]
    Training a deep autoencoder or a classifier on MNIST digits[Project]
    Deep Learning 大牛Geoffrey E. Hinton's HomePage[Website]
十一、目标、行为识别Object, Action Recognition:
     Action Recognition by Dense Trajectories[Project][Code]
     Action Recognition Using a Distributed Representation of Pose and Appearance[Project]
     Recognition Using Regions[Paper][Code]
     2D Articulated Human Pose Estimation[Project]
     Fast Human Pose Estimation Using Appearance and Motion via Multi-Dimensional Boosting Regression[Paper][Code]
     Estimating Human Pose from Occluded Images[Paper][Code]
     Quasi-dense wide baseline matching[Project]
     ChaLearn Gesture Challenge: Principal motion: PCA-based reconstruction of motion histograms[Prpject]
十二、图像处理:
     Distance Transforms of Sampled Functions[Project]
    The Computer Vision Homepage[Project]
十三、一些实用工具:
     EGT: a Toolbox for Multiple View Geometry and Visual Servoing[Project] [Code]
     a development kit of matlab mex functions for OpenCV library[Project]
     Fast Artificial Neural Network Library[Project





https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html

Maintained by Jia-Bin Huang



3D Computer Vision: Past, Present, and FutureTalk3D Computer Visionhttp://www.youtube.com/watch?v=kyIzMr917RcSteven Seitz, University of Washington, Google Tech Talk, 2011                   
Computer Vision and 3D Perception for RoboticsTutorial3D perceptionhttp://www.willowgarage.com/workshops/2010/eccvRadu Bogdan Rusu, Gary Bradski, Caroline Pantofaru, Stefan Hinterstoisser, Stefan Holzer, Kurt Konolige  and Andrea Vedaldi, ECCV 2010 Tutorial 
3D point cloud processing: PCL (Point Cloud Library)Tutorial3D point cloud processinghttp://www.pointclouds.org/media/iccv2011.htmlR. Rusu, S. Holzer, M. Dixon, V. Rabaud, ICCV 2011 Tutorial 
Looking at people: The past, the present and the futureTutorialAction Recognitionhttp://www.cs.brown.edu/~ls/iccv2011tutorial.htmlL. Sigal, T. Moeslund, A. Hilton, V. Kruger, ICCV 2011 Tutorial 
Frontiers of Human Activity AnalysisTutorialAction Recognitionhttp://cvrc.ece.utexas.edu/mryoo/cvpr2011tutorial/J. K. Aggarwal, Michael S. Ryoo, and Kris Kitani, CVPR 2011 Tutorial 
Statistical and Structural Recognition of Human ActionsTutorialAction Recognitionhttps://sites.google.com/site/humanactionstutorialeccv10/Ivan Laptev and Greg Mori, ECCV 2010 Tutorial 
Dense Trajectories Video DescriptionCodeAction Recognitionhttp://lear.inrialpes.fr/people/wang/dense_trajectoriesH. Wang and A. Klaser and C. Schmid and C.- L. Liu, Action Recognition by Dense Trajectories, CVPR, 2011 
3D Gradients (HOG3D)CodeAction Recognitionhttp://lear.inrialpes.fr/people/klaeser/research_hog3dA. Klaser, M. Marszałek, and C. Schmid, BMVC, 2008. 
Spectral MattingCodeAlpha Mattinghttp://www.vision.huji.ac.il/SpectralMatting/A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008 
Learning-based MattingCodeAlpha Mattinghttp://www.mathworks.com/matlabcentral/fileexchange/31412Y. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009 
Bayesian MattingCodeAlpha Mattinghttp://www1.idc.ac.il/toky/CompPhoto-09/Projects/Stud_projects/Miki/index.htmlY. Y. Chuang, B. Curless, D. H. Salesin, and R. Szeliski, A Bayesian Approach to Digital Matting, CVPR, 2001 
Closed Form MattingCodeAlpha Mattinghttp://people.csail.mit.edu/alevin/matting.tar.gzA. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting, PAMI 2008. 
Shared MattingCodeAlpha Mattinghttp://www.inf.ufrgs.br/~eslgastal/SharedMatting/E. S. L. Gastal and M. M. Oliveira, Computer Graphics Forum, 2010 
Introduction To Bayesian InferenceTalkBayesian Inferencehttp://videolectures.net/mlss09uk_bishop_ibi/Christopher Bishop, Microsoft Research 
Modern Bayesian NonparametricsTalkBayesian Nonparametricshttp://www.youtube.com/watch?v=F0_ih7THV94&feature=relmfuPeter Orbanz and Yee Whye Teh 
Theory and Applications of BoostingTalkBoostinghttp://videolectures.net/mlss09us_schapire_tab/Robert Schapire, Department of Computer Science, Princeton University 
Epipolar Geometry ToolboxCodeCamera Calibrationhttp://egt.dii.unisi.it/G.L. Mariottini, D. Prattichizzo, EGT: a Toolbox for Multiple View Geometry and Visual Servoing, IEEE Robotics & Automation Magazine, 2005 
Camera Calibration Toolbox for MatlabCodeCamera Calibrationhttp://www.vision.caltech.edu/bouguetj/calib_doc/http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/ref.html 
EasyCamCalibCodeCamera Calibrationhttp://arthronav.isr.uc.pt/easycamcalib/J. Barreto, J. Roquette, P. Sturm, and F. Fonseca, Automatic camera calibration applied to medical endoscopy, BMVC, 2009 
Spectral Clustering - UCSD ProjectCodeClusteringhttp://vision.ucsd.edu/~sagarwal/spectral-0.2.tgz 
K-Means - Oxford CodeCodeClusteringhttp://www.cs.ucf.edu/~vision/Code/vggkmeans.zip 
Self-Tuning Spectral ClusteringCodeClusteringhttp://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.html 
K-Means - VLFeatCodeClusteringhttp://www.vlfeat.org/ 
Spectral Clustering - UW ProjectCodeClusteringhttp://www.stat.washington.edu/spectral/ 
Color image understanding: from acquisition to high-level image understandingTutorialColor Image Processinghttp://www.cat.uab.cat/~joost/tutorial_iccv.htmlTheo Gevers, Keigo Hirakawa, Joost van de Weijer, ICCV 2011 Tutorial 
Sketching the CommonCodeCommon Visual Pattern Discoveryhttp://www.wisdom.weizmann.ac.il/~bagon/matlab_code/SketchCommonCVPR10_v1.1.tar.gzS. Bagon, O. Brostovsky, M. Galun and M. Irani, Detecting and Sketching the Common, CVPR 2010 
Common Visual Pattern Discovery via Spatially Coherent CorrespondencesCodeCommon Visual Pattern Discoveryhttps://sites.google.com/site/lhrbss/home/papers/SimplifiedCode.zip?attredirects=0H. Liu, S. Yan, "Common Visual Pattern Discovery via Spatially Coherent Correspondences", CVPR 2010 
Fcam: an architecture and API for computational camerasTutorialComputational Imaginghttp://fcam.garage.maemo.org/iccv2011.htmlKari Pulli, Andrew Adams, Timo Ahonen, Marius Tico, ICCV 2011 Tutorial 
Computational Photography, University of Illinois, Urbana-Champaign, Fall 2011CourseComputational Photographyhttp://www.cs.illinois.edu/class/fa11/cs498dh/Derek Hoiem 
Computational Photography, CMU, Fall 2011CourseComputational Photographyhttp://graphics.cs.cmu.edu/courses/15-463/2011_fall/463.htmlAlexei “Alyosha” Efros 
Computational Symmetry: Past, Current, FutureTutorialComputational Symmetryhttp://vision.cse.psu.edu/research/symmComp/index.shtmlYanxi Liu, ECCV 2010 Tutorial 
Introduction to Computer Vision, Stanford University, Winter 2010-2011CourseComputer Visionhttp://vision.stanford.edu/teaching/cs223b/Fei-Fei Li 
Computer Vision: From 3D Reconstruction to Visual Recognition, Fall 2012CourseComputer Visionhttps://www.coursera.org/course/computervisionSilvio Savarese and Fei-Fei Li 
Computer Vision, University of Texas at Austin, Spring 2011CourseComputer Visionhttp://www.cs.utexas.edu/~grauman/courses/spring2011/index.htmlKristen Grauman 
Learning-Based Methods in Vision, CMU, Spring 2012CourseComputer Visionhttps://docs.google.com/document/pub?id=1jGBn7zPDEaU33fJwi3YI_usWS-U6gpSSJotV_2gDrL0Alexei “Alyosha” Efros and Leonid Sigal 
Introduction to Computer VisionCourseComputer Visionhttp://www.cs.brown.edu/courses/cs143/James Hays, Brown University, Fall 2011 
Computer Image Analysis, Computer Vision ConferencesLinkComputer Visionhttp://iris.usc.edu/information/Iris-Conferences.htmlUSC 
CV Papers on the webLinkComputer Visionhttp://www.cvpapers.com/index.htmlCVPapers 
Computer Vision, University of North Carolina at Chapel Hill, Spring 2010CourseComputer Visionhttp://www.cs.unc.edu/~lazebnik/spring10/Svetlana Lazebnik 
CVonlineLinkComputer Visionhttp://homepages.inf.ed.ac.uk/rbf/CVonline/CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision 
Computer Vision: The Fundamentals, University of California at Berkeley, Fall 2012CourseComputer Visionhttps://www.coursera.org/course/visionJitendra Malik 
Computer Vision, New York University, Fall 2012CourseComputer Visionhttp://cs.nyu.edu/~fergus/teaching/vision_2012/index.htmlRob Fergus 
Advances in Computer VisionCourseComputer Visionhttp://groups.csail.mit.edu/vision/courses/6.869/Antonio Torralba, MIT, Spring 2010 
Annotated Computer Vision BibliographyLinkComputer Visionhttp://iris.usc.edu/Vision-Notes/bibliography/contents.htmlcompiled by Keith Price 
Computer Vision, University of Illinois, Urbana-Champaign, Spring 2012CourseComputer Visionhttp://www.cs.illinois.edu/class/sp12/cs543/Derek Hoiem 
The Computer Vision homepageLinkComputer Visionhttp://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html 
Computer Vision, University of Washington, Winter 2012CourseComputer Visionhttp://www.cs.washington.edu/education/courses/cse455/12wi/Steven Seitz 
CV Datasets on the webLinkComputer Visionhttp://www.cvpapers.com/datasets.htmlCVPapers 
The Computer Vision IndustryLinkComputer Vision Industryhttp://www.cs.ubc.ca/~lowe/vision.htmlDavid Lowe 
Compiled list of recognition datasetsLinkDatasethttp://www.cs.utexas.edu/~grauman/courses/spring2008/datasets.htmcompiled by Kristen Grauman 
Decision forests for classification, regression, clustering and density estimationTutorialDecision Forestshttp://research.microsoft.com/en-us/groups/vision/decisionforests.aspxA. Criminisi, J. Shotton and E. Konukoglu, ICCV 2011 Tutorial 
A tutorial on Deep LearningTalkDeep Learninghttp://videolectures.net/jul09_hinton_deeplearn/Geoffrey E. Hinton, Department of Computer Science, University of Toronto 
Kernel Density Estimation ToolboxCodeDensity Estimationhttp://www.ics.uci.edu/~ihler/code/kde.html 
Kinect SDKCodeDepth Sensorhttp://www.microsoft.com/en-us/kinectforwindows/http://www.microsoft.com/en-us/kinectforwindows/ 
LLECodeDimension Reductionhttp://www.cs.nyu.edu/~roweis/lle/code.html 
Laplacian EigenmapsCodeDimension Reductionhttp://www.cse.ohio-state.edu/~mbelkin/algorithms/Laplacian.tar 
Diffusion mapsCodeDimension Reductionhttp://www.stat.cmu.edu/~annlee/software.htm 
ISOMAPCodeDimension Reductionhttp://isomap.stanford.edu/ 
Dimensionality Reduction ToolboxCodeDimension Reductionhttp://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html 
Matlab Toolkit for Distance Metric LearningCodeDistance Metric Learninghttp://www.cs.cmu.edu/~liuy/distlearn.htm 
Distance Functions and Metric LearningTutorialDistance Metric Learninghttp://www.cs.huji.ac.il/~ofirpele/DFML_ECCV2010_tutorial/M. Werman, O. Pele and  B. Kulis, ECCV 2010 Tutorial 
Distance Transforms of Sampled FunctionsCodeDistance Transformationhttp://people.cs.uchicago.edu/~pff/dt/ 
Hidden Markov ModelsTutorialExpectation Maximizationhttp://crow.ee.washington.edu/people/bulyko/papers/em.pdfJeff A. Bilmes, University of California at Berkeley 
Edge Foci Interest PointsCodeFeature Detectionhttp://research.microsoft.com/en-us/um/people/larryz/edgefoci/edge_foci.htmL. Zitnickand K. Ramnath, Edge Foci Interest Points, ICCV, 2011 
Boundary Preserving Dense Local RegionsCodeFeature Detectionhttp://vision.cs.utexas.edu/projects/bplr/bplr.htmlJ. Kim and K. Grauman, Boundary Preserving Dense Local Regions, CVPR 2011 
Canny Edge DetectionCodeFeature Detectionhttp://www.mathworks.com/help/toolbox/images/ref/edge.htmlJ. Canny, A Computational Approach To Edge Detection, PAMI, 1986 
FAST Corner DetectionCodeFeature Detectionhttp://www.edwardrosten.com/work/fast.htmlE. Rosten and T. Drummond, Machine learning for high-speed corner detection, ECCV, 2006 
Groups of Adjacent Contour SegmentsCodeFeature Detection; Feature Extractionhttp://www.robots.ox.ac.uk/~vgg/share/ferrari/release-kas-v102.tgzV. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, Groups of Adjacent Contour Segments for Object Detection, PAMI, 2007 
Maximally stable extremal regions (MSER) - VLFeatCodeFeature Detection; Feature Extractionhttp://www.vlfeat.org/J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002 
Geometric BlurCodeFeature Detection; Feature Extractionhttp://www.robots.ox.ac.uk/~vgg/software/MKL/A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR, 2005 
Affine-SIFTCodeFeature Detection; Feature Extractionhttp://www.ipol.im/pub/algo/my_affine_sift/J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009 
Scale-invariant feature transform (SIFT) - Demo SoftwareCodeFeature Detection; Feature Extractionhttp://www.cs.ubc.ca/~lowe/keypoints/D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. 
Affine Covariant FeaturesCodeFeature Detection; Feature Extractionhttp://www.robots.ox.ac.uk/~vgg/research/affine/T. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors: A Survey, Foundations and Trends in Computer Graphics and Vision, 2008 
Scale-invariant feature transform (SIFT) - LibraryCodeFeature Detection; Feature Extractionhttp://blogs.oregonstate.edu/hess/code/sift/D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. 
Maximally stable extremal regions (MSER)CodeFeature Detection; Feature Extractionhttp://www.robots.ox.ac.uk/~vgg/research/affine/J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002 
Color DescriptorCodeFeature Detection; Feature Extractionhttp://koen.me/research/colordescriptors/K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene Recognition, PAMI, 2010 
Speeded Up Robust Feature (SURF) - Open SURFCodeFeature Detection; Feature Extractionhttp://www.chrisevansdev.com/computer-vision-opensurf.htmlH. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006 
Scale-invariant feature transform (SIFT) - VLFeatCodeFeature Detection; Feature Extractionhttp://www.vlfeat.org/D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. 
Speeded Up Robust Feature (SURF) - Matlab WrapperCodeFeature Detection; Feature Extractionhttp://www.maths.lth.se/matematiklth/personal/petter/surfmex.phpH. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006 
Space-Time Interest Points (STIP)CodeFeature Detection; Feature Extraction; Action Recognitionhttp://www.irisa.fr/vista/Equipe/People/Laptev/download/stip-1.1-winlinux.zip;http://www.nada.kth.se/cvap/abstracts/cvap284.htmlI. Laptev, On Space-Time Interest Points, IJCV, 2005; I. Laptev and T. Lindeberg, On Space-Time Interest Points, IJCV 2005 
PCA-SIFTCodeFeature Extractionhttp://www.cs.cmu.edu/~yke/pcasift/Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004 
sRD-SIFTCodeFeature Extractionhttp://arthronav.isr.uc.pt/~mlourenco/srdsift/index.html#M. Lourenco, J. P. Barreto and A. Malti, Feature Detection and Matching in Images with Radial Distortion, ICRA 2010 
Local Self-Similarity DescriptorCodeFeature Extractionhttp://www.robots.ox.ac.uk/~vgg/software/SelfSimilarity/E. Shechtman and M. Irani. Matching local self-similarities across images and videos, CVPR, 2007 
Pyramids of Histograms of Oriented Gradients (PHOG)CodeFeature Extractionhttp://www.robots.ox.ac.uk/~vgg/research/caltech/phog/phog.zipA. Bosch, A. Zisserman, and X. Munoz, Representing shape with a spatial pyramid kernel, CIVR, 2007 
BRIEF: Binary Robust Independent Elementary FeaturesCodeFeature Extractionhttp://cvlab.epfl.ch/research/detect/brief/M. Calonder, V. Lepetit, C. Strecha, P. Fua, BRIEF: Binary Robust Independent Elementary Features, ECCV 2010 
Global and Efficient Self-SimilarityCodeFeature Extractionhttp://www.vision.ee.ethz.ch/~calvin/gss/selfsim_release1.0.tgzT. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection. CVPR 2010; T. Deselaers, V. Ferrari, Global and Efficient Self-Similarity for Object Classification and Detection, CVPR 2010
GIST DescriptorCodeFeature Extractionhttp://people.csail.mit.edu/torralba/code/spatialenvelope/A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, IJCV, 2001 
Shape ContextCodeFeature Extractionhttp://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sc_digits.htmlS. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002 
Image and Video Description with Local Binary Pattern VariantsTutorialFeature Extractionhttp://www.ee.oulu.fi/research/imag/mvg/files/pdf/CVPR-tutorial-final.pdfM. Pietikainen and J. Heikkila, CVPR 2011 Tutorial 
Histogram of Oriented Graidents - OLT for windowsCodeFeature Extraction; Object Detectionhttp://www.computing.edu.au/~12482661/hog.htmlN. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005 
Histogram of Oriented Graidents - INRIA Object Localization ToolkitCodeFeature Extraction; Object Detectionhttp://www.navneetdalal.com/softwareN. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005 
Feature Learning for Image ClassificationTutorialFeature Learning, Image Classificationhttp://ufldl.stanford.edu/eccv10-tutorial/Kai Yu and Andrew Ng, ECCV 2010 Tutorial 
The Pyramid Match: Efficient Matching for Retrieval and RecognitionCodeFeature Matching; Image Classificationhttp://www.cs.utexas.edu/~grauman/research/projects/pmk/pmk_projectpage.htmK. Grauman and T. Darrell.  The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005 
Game Theory in Computer Vision and Pattern RecognitionTutorialGame Theoryhttp://www.dsi.unive.it/~atorsell/cvpr2011tutorial/Marcello Pelillo and Andrea Torsello, CVPR 2011 Tutorial 
Gaussian Process BasicsTalkGaussian Processhttp://videolectures.net/gpip06_mackay_gpb/David MacKay, University of Cambridge 
Hyper-graph Matching via Reweighted Random WalksCodeGraph Matchinghttp://cv.snu.ac.kr/research/~RRWHM/J. Lee, M. Cho, K. M. Lee. "Hyper-graph Matching via Reweighted Random Walks", CVPR 2011 
Reweighted Random Walks for Graph MatchingCodeGraph Matchinghttp://cv.snu.ac.kr/research/~RRWM/M. Cho, J. Lee, and K. M. Lee, Reweighted Random Walks for Graph Matching, ECCV 2010 
Learning with inference for discrete graphical modelsTutorialGraphical Modelshttp://www.csd.uoc.gr/~komod/ICCV2011_tutorial/Nikos Komodakis, Pawan Kumar, Nikos Paragios, Ramin Zabih, ICCV 2011 Tutorial 
Graphical Models and message-passing algorithmsTalkGraphical Modelshttp://videolectures.net/mlss2011_wainwright_messagepassing/Martin J. Wainwright, University of California at Berkeley 
Graphical Models, Exponential Families, and Variational InferenceTutorialGraphical Modelshttp://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdfMartin J. Wainwright and Michael I. Jordan, University of California at Berkeley 
Inference in Graphical Models, Stanford University, Spring 2012CourseGraphical Modelshttp://www.stanford.edu/~montanar/TEACHING/Stat375/stat375.htmlAndrea Montanari, Stanford University 
Ground shadow detectionCodeIllumination, Reflectance, and Shadowhttp://www.jflalonde.org/software.html#shadowDetectionJ.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs, ECCV 2010 
Estimating Natural Illumination from a Single Outdoor ImageCodeIllumination, Reflectance, and Shadowhttp://www.cs.cmu.edu/~jlalonde/software.html#skyModelJ-F. Lalonde, A. A. Efros, S. G. Narasimhan, Estimating Natural Illumination from a Single Outdoor Image , ICCV 2009 
What Does the Sky Tell Us About the Camera?CodeIllumination, Reflectance, and Shadowhttp://www.cs.cmu.edu/~jlalonde/software.html#skyModelJ-F. Lalonde, S. G. Narasimhan, A. A. Efros,  What Does the Sky Tell Us About the Camera?, ECCV 2008 
Shadow Detection using Paired RegionCodeIllumination, Reflectance, and Shadowhttp://www.cs.illinois.edu/homes/guo29/projects/shadow.htmlR. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011 
Real-time Specular Highlight RemovalCodeIllumination, Reflectance, and Shadowhttp://www.cs.cityu.edu.hk/~qiyang/publications/code/eccv-10.zipQ. Yang, S. Wang and N. Ahuja, Real-time Specular Highlight Removal Using Bilateral Filtering, ECCV 2010 
Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse SequencesCodeIllumination, Reflectance, and Shadowhttp://www.cs.cmu.edu/~jlalonde/software.html#skyModelJ-F. Lalonde, A. A. Efros, S. G. Narasimhan, Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse Sequences, SIGGRAPH Asia 2009 
Sparse Coding for Image ClassificationCodeImage Classificationhttp://www.ifp.illinois.edu/~jyang29/ScSPM.htmJ. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification, CVPR, 2009 
Texture ClassificationCodeImage Classificationhttp://www.robots.ox.ac.uk/~vgg/research/texclass/index.htmlM. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005 
Locality-constrained Linear CodingCodeImage Classificationhttp://www.ifp.illinois.edu/~jyang29/LLC.htmJ. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification, CVPR, 2010 
Spatial Pyramid MatchingCodeImage Classificationhttp://www.cs.unc.edu/~lazebnik/research/SpatialPyramid.zipS. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, CVPR 2006 
Non-blind deblurring (and blind denoising) with integrated noise estimationCodeImage Deblurringhttp://www.gris.tu-darmstadt.de/research/visinf/software/index.en.htmU. Schmidt, K. Schelten, and S. Roth. Bayesian deblurring with integrated noise estimation, CVPR 2011 
Richardson-Lucy Deblurring for Scenes under Projective Motion PathCodeImage Deblurringhttp://yuwing.kaist.ac.kr/projects/projectivedeblur/projectivedeblur_files/ProjectiveDeblur.zipY.-W. Tai, P. Tan, M. S. Brown: Richardson-Lucy Deblurring for Scenes under Projective Motion Path, PAMI 2011 
Analyzing spatially varying blurCodeImage Deblurringhttp://www.eecs.harvard.edu/~ayanc/svblur/A. Chakrabarti, T. Zickler, and W. T. Freeman, Analyzing Spatially-varying Blur, CVPR 2010 
Radon TransformCodeImage Deblurringhttp://people.csail.mit.edu/taegsang/Documents/RadonDeblurringCode.zipT. S. Cho, S. Paris, B. K. P. Horn, W. T. Freeman, Blur kernel estimation using the radon transform, CVPR 2011 
Eficient Marginal Likelihood Optimization in Blind DeconvolutionCodeImage Deblurringhttp://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR2011Code.zipA. Levin, Y. Weiss, F. Durand, W. T. Freeman. Efficient Marginal Likelihood Optimization in Blind Deconvolution, CVPR 2011 
BLS-GSMCodeImage Denoisinghttp://decsai.ugr.es/~javier/denoise/ 
Gaussian Field of ExpertsCodeImage Denoisinghttp://www.cs.huji.ac.il/~yweiss/BRFOE.zip 
Field of ExpertsCodeImage Denoisinghttp://www.cs.brown.edu/~roth/research/software.html 
BM3DCodeImage Denoisinghttp://www.cs.tut.fi/~foi/GCF-BM3D/ 
Nonlocal means with cluster treesCodeImage Denoisinghttp://lmb.informatik.uni-freiburg.de/resources/binaries/nlmeans_brox_tip08Linux64.zipT. Brox, O. Kleinschmidt, D. Cremers, Efficient nonlocal means for denoising of textural patterns, TIP 2008 
Non-local MeansCodeImage Denoisinghttp://dmi.uib.es/~abuades/codis/NLmeansfilter.m 
K-SVDCodeImage Denoisinghttp://www.cs.technion.ac.il/~ronrubin/Software/ksvdbox13.zip 
What makes a good model of natural images ?CodeImage Denoisinghttp://www.cs.huji.ac.il/~yweiss/BRFOE.zipY. Weiss and W. T. Freeman, CVPR 2007 
Clustering-based DenoisingCodeImage Denoisinghttp://users.soe.ucsc.edu/~priyam/K-LLD/P. Chatterjee and P. Milanfar, Clustering-based Denoising with Locally Learned Dictionaries (K-LLD), TIP, 2009 
Sparsity-based Image DenoisingCodeImage Denoisinghttp://www.csee.wvu.edu/~xinl/CSR.htmlW. Dong, X. Li, L. Zhang and G. Shi, Sparsity-based Image Denoising vis Dictionary Learning and Structural Clustering, CVPR, 2011 
Kernel RegressionsCodeImage Denoisinghttp://www.soe.ucsc.edu/~htakeda/MatlabApp/KernelRegressionBasedImageProcessingToolBox_ver1-1beta.zip 
Learning Models of Natural Image PatchesCodeImage Denoising; Image Super-resolution; Image Deblurringhttp://www.cs.huji.ac.il/~daniez/D. Zoran and Y. Weiss, From Learning Models of Natural Image Patches to Whole Image Restoration, ICCV, 2011 
Efficient Belief Propagation for Early VisionCodeImage Denoising; Stereo Matchinghttp://www.cs.brown.edu/~pff/bp/P. F. Felzenszwalb and D. P. Huttenlocher, Efficient Belief Propagation for Early Vision, IJCV, 2006 
SVM for Edge-Preserving FilteringCodeImage Filteringhttp://vision.ai.uiuc.edu/~qyang6/publications/code/cvpr-10-svmbf/program_video_conferencing.zipQ. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering, 
Local Laplacian FiltersCodeImage Filteringhttp://people.csail.mit.edu/sparis/publi/2011/siggraph/matlab_source_code.zipS. Paris, S. Hasinoff, J. Kautz, Local Laplacian Filters: Edge-Aware Image Processing with a Laplacian Pyramid, SIGGRAPH 2011 
Real-time O(1) Bilateral FilteringCodeImage Filteringhttp://vision.ai.uiuc.edu/~qyang6/publications/code/qx_constant_time_bilateral_filter_ss.zipQ. Yang, K.-H. Tan and N. Ahuja,  Real-time O(1) Bilateral Filtering, 
Image smoothing via L0 Gradient MinimizationCodeImage Filteringhttp://www.cse.cuhk.edu.hk/~leojia/projects/L0smoothing/L0smoothing.zipL. Xu, C. Lu, Y. Xu, J. Jia, Image smoothing via L0 Gradient Minimization, SIGGRAPH Asia 2011 
Anisotropic DiffusionCodeImage Filteringhttp://www.mathworks.com/matlabcentral/fileexchange/14995-anisotropic-diffusion-perona-malikP. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, PAMI 1990 
Guided Image FilteringCodeImage Filteringhttp://personal.ie.cuhk.edu.hk/~hkm007/eccv10/guided-filter-code-v1.rarK. He, J. Sun, X. Tang, Guided Image Filtering, ECCV 2010 
Fast Bilateral FilterCodeImage Filteringhttp://people.csail.mit.edu/sparis/bf/S. Paris and F. Durand, A Fast Approximation of the Bilateral Filter using a Signal Processing Approach, ECCV, 2006 
GradientShopCodeImage Filteringhttp://grail.cs.washington.edu/projects/gradientshop/P. Bhat, C.L. Zitnick, M. Cohen, B. Curless, and J. Kim, GradientShop: A Gradient-Domain Optimization Framework for Image and Video Filtering, TOG 2010 
Domain TransformationCodeImage Filteringhttp://inf.ufrgs.br/~eslgastal/DomainTransform/DomainTransformFilters-Source-v1.0.zipE. Gastal, M. Oliveira, Domain Transform for Edge-Aware Image and Video Processing, SIGGRAPH 2011 
Weighted Least Squares FilterCodeImage Filteringhttp://www.cs.huji.ac.il/~danix/epd/Z. Farbman, R. Fattal, D. Lischinski, R. Szeliski, Edge-Preserving Decompositions for Multi-Scale Tone and Detail Manipulation, SIGGRAPH 2008 
Piotr's Image & Video Matlab ToolboxCodeImage Processing; Image Filteringhttp://vision.ucsd.edu/~pdollar/toolbox/doc/index.htmlPiotr Dollar, Piotr's Image & Video Matlab Toolbox,http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html 
Structural SIMilarityCodeImage Quality Assessmenthttps://ece.uwaterloo.ca/~z70wang/research/ssim/ 
SPIQACodeImage Quality Assessmenthttp://vision.ai.uiuc.edu/~bghanem2/shared_code/SPIQA_code.zip 
Feature SIMilarity IndexCodeImage Quality Assessmenthttp://www4.comp.polyu.edu.hk/~cslzhang/IQA/FSIM/FSIM.htm 
Degradation ModelCodeImage Quality Assessmenthttp://users.ece.utexas.edu/~bevans/papers/2000/imageQuality/index.html 
Tools and Methods for Image RegistrationTutorialImage Registrationhttp://www.imgfsr.com/CVPR2011/Tutorial6/Brown, 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 Tutorial 
SLIC SuperpixelsCodeImage Segmentationhttp://ivrg.epfl.ch/supplementary_material/RK_SLICSuperpixels/index.htmlR. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010 
Recovering Occlusion Boundaries from a Single ImageCodeImage Segmentationhttp://www.cs.cmu.edu/~dhoiem/software/D. Hoiem, A. Stein, A. A. Efros, M. Hebert, Recovering Occlusion Boundaries from a Single Image, ICCV 2007. 
Multiscale Segmentation TreeCodeImage Segmentationhttp://vision.ai.uiuc.edu/segmentationE. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,”  ACCV 2009; N. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996 
Quick-ShiftCodeImage Segmentationhttp://www.vlfeat.org/overview/quickshift.htmlA. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking, ECCV, 2008 
Efficient Graph-based Image Segmentation - C++ codeCodeImage Segmentationhttp://people.cs.uchicago.edu/~pff/segment/P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004 
TurbepixelsCodeImage Segmentationhttp://www.cs.toronto.edu/~babalex/research.htmlA. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric Flows, PAMI 2009 
Superpixel by Gerg MoriCodeImage Segmentationhttp://www.cs.sfu.ca/~mori/research/superpixels/X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003 
Normalized CutCodeImage Segmentationhttp://www.cis.upenn.edu/~jshi/software/J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000 
Mean-Shift Image Segmentation - Matlab WrapperCodeImage Segmentationhttp://www.wisdom.weizmann.ac.il/~bagon/matlab_code/edison_matlab_interface.tar.gzD. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002 
Segmenting Scenes by Matching Image CompositesCodeImage Segmentationhttp://www.cs.washington.edu/homes/bcr/projects/SceneComposites/index.htmlB. Russell, A. A. Efros, J.  Sivic, W. T. Freeman, A. Zisserman, NIPS 2009 
OWT-UCM Hierarchical SegmentationCodeImage Segmentationhttp://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.htmlP. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011 
Entropy Rate Superpixel SegmentationCodeImage Segmentationhttp://www.umiacs.umd.edu/~mingyliu/src/ers_matlab_wrapper_v0.1.zipM.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011 
Efficient Graph-based Image Segmentation - Matlab WrapperCodeImage Segmentationhttp://www.mathworks.com/matlabcentral/fileexchange/25866-efficient-graph-based-image-segmentationP. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004 
Biased Normalized CutCodeImage Segmentationhttp://www.cs.berkeley.edu/~smaji/projects/biasedNcuts/S. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut, CVPR 2011 
Segmentation by Minimum Code LengthCodeImage Segmentationhttp://perception.csl.uiuc.edu/coding/image_segmentation/A. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression, CVIU, 2007 
Mean-Shift Image Segmentation - EDISONCodeImage Segmentationhttp://coewww.rutgers.edu/riul/research/code/EDISON/index.htmlD. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002 
Self-Similarities for Single Frame Super-ResolutionCodeImage Super-resolutionhttps://eng.ucmerced.edu/people/cyang35/ACCV10.zipC.-Y. Yang, J.-B. Huang, and M.-H. Yang, Exploiting Self-Similarities for Single Frame Super-Resolution, ACCV 2010 
MRF for image super-resolutionCodeImage Super-resolutionhttp://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.htmlW. T Freeman and C. Liu. Markov Random Fields for Super-resolution and Texture Synthesis. In A. Blake, P. Kohli, and C. Rother, eds., Advances in Markov Random Fields for Vision and Image Processing, Chapter 10. MIT Press, 2011
Sprarse coding super-resolutionCodeImage Super-resolutionhttp://www.ifp.illinois.edu/~jyang29/ScSR.htmJ. Yang, J. Wright, T. S. Huang, and Y. Ma. Image super-resolution via sparse representation, TIP 2010 
Multi-frame image super-resolutionCodeImage Super-resolutionhttp://www.robots.ox.ac.uk/~vgg/software/SR/index.htmlPickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis 
Single-Image Super-Resolution Matlab PackageCodeImage Super-resolutionhttp://www.cs.technion.ac.il/~elad/Various/Single_Image_SR.zipR. Zeyde, M. Elad, and M. Protter, On Single Image Scale-Up using Sparse-Representations, LNCS 2010 
MDSP Resolution Enhancement SoftwareCodeImage Super-resolutionhttp://users.soe.ucsc.edu/~milanfar/software/superresolution.htmlS. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Fast and Robust Multi-frame Super-resolution, TIP 2004 
Nonparametric Scene Parsing via Label TransferCodeImage Understandinghttp://people.csail.mit.edu/celiu/LabelTransfer/index.htmlC. Liu, J. Yuen, and Antonio Torralba, Nonparametric Scene Parsing via Label Transfer, PAMI 2011 
Discriminative Models for Multi-Class Object LayoutCodeImage Understandinghttp://www.ics.uci.edu/~desaic/multiobject_context.zipC. Desai, D. Ramanan, C. Fowlkes. "Discriminative Models for Multi-Class Object Layout, IJCV 2011 
Towards Total Scene UnderstandingCodeImage Understandinghttp://vision.stanford.edu/projects/totalscene/index.htmlL.-J. Li, R. Socher and Li F.-F.. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework, CVPR 2009 
Object BankCodeImage Understandinghttp://vision.stanford.edu/projects/objectbank/index.htmlLi-Jia Li, Hao Su, Eric P. Xing and Li Fei-Fei. Object Bank: A High-Level Image Representation for Scene Classification and Semantic Feature Sparsification, NIPS 2010 
SuperParsingCodeImage Understandinghttp://www.cs.unc.edu/~jtighe/Papers/ECCV10/eccv10-jtighe-code.zipJ. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image 
Blocks World Revisited: Image Understanding using Qualitative Geometry and MechanicsCodeImage Understandinghttp://www.cs.cmu.edu/~abhinavg/blocksworld/#downloadsA. Gupta, A. A. Efros, M. Hebert, Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics, ECCV 2010 
Information TheoryTalkInformation Theoryhttp://videolectures.net/mlss09uk_mackay_it/David MacKay, University of Cambridge 
Information Theory in Learning and ControlTalkInformation Theoryhttp://www.youtube.com/watch?v=GKm53xGbAOk&feature=relmfuNaftali (Tali) Tishby, The Hebrew University 
Efficient Earth Mover's Distance with L1 Ground Distance (EMD_L1)CodeKernels and Distanceshttp://www.dabi.temple.edu/~hbling/code/EmdL1_v3.zipH. Ling and K. Okada, An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison, PAMI 2007 
Machine learning and kernel methods for computer visionTalkKernels and Distanceshttp://videolectures.net/etvc08_bach_mlakm/Francis R. Bach, INRIA 
Diffusion-based distanceCodeKernels and Distanceshttp://www.dabi.temple.edu/~hbling/code/DD_v1.zipH. Ling and K. Okada, Diffusion Distance for Histogram Comparison, CVPR 2006 
Fast Directional Chamfer MatchingCodeKernels and Distanceshttp://www.umiacs.umd.edu/~mingyliu/src/fdcm_matlab_wrapper_v0.2.zip 
Learning and Inference in Low-Level VisionTalkLow-level visionhttp://videolectures.net/nips09_weiss_lil/Yair Weiss, School of Computer Science and Engineering, The Hebrew University of Jerusalem 
TILT: Transform Invariant Low-rank TexturesCodeLow-Rank Modelinghttp://perception.csl.uiuc.edu/matrix-rank/tilt.htmlZ. Zhang, A. Ganesh, X. Liang, and Y. Ma, TILT: Transform Invariant Low-rank Textures, IJCV 2011 
Low-Rank Matrix Recovery and CompletionCodeLow-Rank Modelinghttp://perception.csl.uiuc.edu/matrix-rank/sample_code.html 
RASL: Robust Batch Alignment of Images by Sparse and Low-Rank DecompositionCodeLow-Rank Modelinghttp://perception.csl.uiuc.edu/matrix-rank/rasl.htmlY. Peng, A. Ganesh, J. Wright, W. Xu, and Y. Ma, RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition, CVPR 2010 
Statistical Pattern Recognition ToolboxCodeMachine Learninghttp://cmp.felk.cvut.cz/cmp/software/stprtool/M.I. Schlesinger, V. Hlavac: Ten lectures on the statistical and structural pattern recognition, Kluwer Academic Publishers, 2002 
FastICA package for MATLABCodeMachine Learninghttp://research.ics.tkk.fi/ica/fastica/http://research.ics.tkk.fi/ica/book/ 
Boosting Resources by Liangliang CaoCodeMachine Learninghttp://www.ifp.illinois.edu/~cao4/reading/boostingbib.htmhttp://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm 
Netlab Neural Network SoftwareCodeMachine Learninghttp://www1.aston.ac.uk/eas/research/groups/ncrg/resources/netlab/C. M. Bishop, Neural Networks for Pattern RecognitionㄝOxford University Press, 1995 
Matlab TutorialTutorialMatlabhttp://www.cs.unc.edu/~lazebnik/spring10/matlab.intro.htmlDavid Kriegman and Serge Belongie 
Writing Fast MATLAB CodeTutorialMatlabhttp://www.mathworks.com/matlabcentral/fileexchange/5685Pascal Getreuer, Yale University 
MRF Minimization EvaluationCodeMRF Optimizationhttp://vision.middlebury.edu/MRF/R. Szeliski et al., A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, PAMI, 2008 
Max-flow/min-cutCodeMRF Optimizationhttp://vision.csd.uwo.ca/code/maxflow-v3.01.zipY. Boykov and V. Kolmogorov, An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, PAMI 2004 
Planar Graph CutCodeMRF Optimizationhttp://vision.csd.uwo.ca/code/PlanarCut-v1.0.zipF. R. Schmidt, E. Toppe and D. Cremers, Efficient Planar Graph Cuts with Applications in Computer Vision, CVPR 2009 
Max-flow/min-cut for massive gridsCodeMRF Optimizationhttp://vision.csd.uwo.ca/code/regionpushrelabel-v1.03.zipA. Delong and Y. Boykov, A Scalable Graph-Cut Algorithm for N-D Grids, CVPR 2008 
Multi-label optimizationCodeMRF Optimizationhttp://vision.csd.uwo.ca/code/gco-v3.0.zipY. Boykov, O. Verksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001 
Max-flow/min-cut for shape fittingCodeMRF Optimizationhttp://www.csd.uwo.ca/faculty/yuri/Implementations/TouchExpand.zipV. Lempitsky and Y. Boykov, Global Optimization for Shape Fitting, CVPR 2007 
MILISCodeMultiple Instance Learning Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010 
MILESCodeMultiple Instance Learninghttp://infolab.stanford.edu/~wangz/project/imsearch/SVM/PAMI06/Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection. PAMI 2006 
MIForestsCodeMultiple Instance Learninghttp://www.ymer.org/amir/software/milforests/C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010 
DD-SVMCodeMultiple Instance Learning Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 2004 
DOGMACodeMultiple Kernel Learninghttp://dogma.sourceforge.net/F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010 
SHOGUNCodeMultiple Kernel Learninghttp://www.shogun-toolbox.org/S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning. JMLR, 2006 
SimpleMKLCodeMultiple Kernel Learninghttp://asi.insa-rouen.fr/enseignants/~arakotom/code/mklindex.htmlA. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl. JMRL, 2008 
OpenKernel.orgCodeMultiple Kernel Learninghttp://www.openkernel.org/F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011 
Matlab Functions for Multiple View GeometryCodeMultiple View Geometryhttp://www.robots.ox.ac.uk/~vgg/hzbook/code/ 
for Computer Vision and Image ProcessingCodeMultiple View Geometryhttp://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.htmlP. D. Kovesi.   MATLAB and Octave Functions for Computer Vision and Image Processing, http://www.csse.uwa.edu.au/~pk/research/matlabfns 
Patch-based Multi-view Stereo SoftwareCodeMulti-View Stereohttp://grail.cs.washington.edu/software/pmvs/Y. Furukawa and J. Ponce, Accurate, Dense, and Robust Multi-View Stereopsis, PAMI 2009 
Clustering Views for Multi-view StereoCodeMulti-View Stereohttp://grail.cs.washington.edu/software/cmvs/Y. Furukawa, B. Curless, S. M. Seitz, and R. Szeliski, Towards Internet-scale Multi-view Stereo, CVPR 2010 
Multi-View Stereo EvaluationCodeMulti-View Stereohttp://vision.middlebury.edu/mview/S. Seitz et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006 
Spectral HashingCodeNearest Neighbors Matchinghttp://www.cs.huji.ac.il/~yweiss/SpectralHashing/Y. Weiss, A. Torralba, R. Fergus, Spectral Hashing, NIPS 2008 
FLANN: Fast Library for Approximate Nearest NeighborsCodeNearest Neighbors Matchinghttp://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN 
ANN: Approximate Nearest Neighbor SearchingCodeNearest Neighbors Matchinghttp://www.cs.umd.edu/~mount/ANN/ 
LDAHash: Binary Descriptors for Matching in Large Image DatabasesCodeNearest Neighbors Matchinghttp://cvlab.epfl.ch/research/detect/ldahash/index.phpC. Strecha, A. M. Bronstein, M. M. Bronstein and P. Fua. LDAHash: Improved matching with smaller descriptors, PAMI, 2011. 
Coherency Sensitive HashingCodeNearest Neighbors Matchinghttp://www.eng.tau.ac.il/~simonk/CSH/index.htmlS. Korman, S. Avidan, Coherency Sensitive Hashing, ICCV 2011 
Learning in Hierarchical Architectures: from Neuroscience to Derived KernelsTalkNeurosciencehttp://videolectures.net/mlss09us_poggio_lhandk/Tomaso A. Poggio, McGovern Institute for Brain Research, Massachusetts Institute of Technology 
Computer vision fundamentals: robust non-linear least-squares and their applicationsTutorialNon-linear Least Squareshttp://cvlab.epfl.ch/~fua/courses/lsq/Pascal Fua, Vincent Lepetit, ICCV 2011 Tutorial 
Non-rigid registration and reconstructionTutorialNon-rigid registrationhttp://www.isr.ist.utl.pt/~adb/tutorial/Alessio Del Bue, Lourdes Agapito, Adrien Bartoli, ICCV 2011 Tutorial 
Geometry constrained parts based detectionTutorialObject Detectionhttp://ci2cv.net/tutorials/iccv-2011/Simon Lucey, Jason Saragih, ICCV 2011 Tutorial 
Max-Margin Hough TransformCodeObject Detectionhttp://www.cs.berkeley.edu/~smaji/projects/max-margin-hough/S. Maji and J. Malik, Object Detection Using a Max-Margin Hough Transform. CVPR 2009 
Recognition using regionsCodeObject Detectionhttp://www.cs.berkeley.edu/~chunhui/publications/cvpr09_v2.zipC. Gu, J. J. Lim, P. Arbelaez, and J. Malik, CVPR 2009 
PoseletCodeObject Detectionhttp://www.eecs.berkeley.edu/~lbourdev/poselets/L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009 
A simple object detector with boostingCodeObject Detectionhttp://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.htmlICCV 2005 short courses on Recognizing and Learning Object Categories 
Feature CombinationCodeObject Detectionhttp://www.vision.ee.ethz.ch/~pgehler/projects/iccv09/index.htmlP. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009 
Hough Forests for Object DetectionCodeObject Detectionhttp://www.vision.ee.ethz.ch/~gallju/projects/houghforest/index.htmlJ. Gall and V. Lempitsky, Class-Specific Hough Forests for Object Detection, CVPR, 2009 
Cascade Object Detection with Deformable Part ModelsCodeObject Detectionhttp://people.cs.uchicago.edu/~rbg/star-cascade/P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models. CVPR, 2010 
Discriminatively Trained Deformable Part ModelsCodeObject Detectionhttp://people.cs.uchicago.edu/~pff/latent/P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. 
A simple parts and structure object detectorCodeObject Detectionhttp://people.csail.mit.edu/fergus/iccv2005/partsstructure.htmlICCV 2005 short courses on Recognizing and Learning Object Categories 
Object Recognition with Deformable ModelsTalkObject Detectionhttp://www.youtube.com/watch?v=_J_clwqQ4gIPedro Felzenszwalb, Brown University 
Ensemble of Exemplar-SVMs for Object Detection and BeyondCodeObject Detectionhttp://www.cs.cmu.edu/~tmalisie/projects/iccv11/T. Malisiewicz, A. Gupta, A. A. Efros, Ensemble of Exemplar-SVMs for Object Detection and Beyond , ICCV 2011 
Viola-Jones Object DetectionCodeObject Detectionhttp://pr.willowgarage.com/wiki/FaceDetectionP. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR, 2001 
Implicit Shape ModelCodeObject Detectionhttp://www.vision.ee.ethz.ch/~bleibe/code/ism.htmlB. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation, IJCV, 2008 
Multiple KernelsCodeObject Detectionhttp://www.robots.ox.ac.uk/~vgg/software/MKL/A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection. ICCV, 2009 
Ensemble of Exemplar-SVMsCodeObject Detectionhttp://www.cs.cmu.edu/~tmalisie/projects/iccv11/T. Malisiewicz, A. Gupta, A. Efros. Ensemble of Exemplar-SVMs for Object Detection and Beyond . ICCV, 2011 
Using Multiple Segmentations to Discover Objects and their Extent in Image CollectionsCodeObject Discoveryhttp://people.csail.mit.edu/brussell/research/proj/mult_seg_discovery/index.htmlB. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, Using Multiple Segmentations to Discover Objects and their Extent in Image Collections, CVPR 2006 
Objectness measureCodeObject Proposalhttp://www.vision.ee.ethz.ch/~calvin/objectness/objectness-release-v1.01.tar.gzB. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010 
Parametric min-cutCodeObject Proposalhttp://sminchisescu.ins.uni-bonn.de/code/cpmc/J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010 
Region-based Object ProposalCodeObject Proposalhttp://vision.cs.uiuc.edu/proposals/I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010 
Biologically motivated object recognitionCodeObject Recognitionhttp://cbcl.mit.edu/software-datasets/standardmodel/index.htmlT. Serre, L. Wolf and T. Poggio. Object recognition with features inspired by visual cortex, CVPR 2005 
Recognition by Association via Learning Per-exemplar DistancesCodeObject Recognitionhttp://www.cs.cmu.edu/~tmalisie/projects/cvpr08/dfuns.tar.gzT. Malisiewicz, A. A. Efros, Recognition by Association via Learning Per-exemplar Distances, CVPR 2008 
Sparse to Dense LabelingCodeObject Segmentationhttp://lmb.informatik.uni-freiburg.de/resources/binaries/SparseToDenseLabeling.tar.gzP. Ochs, T. Brox, Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions, ICCV 2011 
ClassCut for Unsupervised Class SegmentationCodeObject Segmentationhttp://www.vision.ee.ethz.ch/~calvin/classcut/ClassCut-release.zipB. Alexe, T. Deselaers and V. Ferrari, ClassCut for Unsupervised Class Segmentation, ECCV 2010 
Geodesic Star Convexity for Interactive Image SegmentationCodeObject Segmentationhttp://www.robots.ox.ac.uk/~vgg/software/iseg/index.shtmlV. Gulshan, C. Rother, A. Criminisi, A. Blake and A. Zisserman. Geodesic star convexity for interactive image segmentation 
Black and Anandan's Optical FlowCodeOptical Flowhttp://www.cs.brown.edu/~dqsun/code/ba.zip 
Optical Flow EvaluationCodeOptical Flowhttp://vision.middlebury.edu/flow/S. Baker et al. A Database and Evaluation Methodology for Optical Flow, IJCV, 2011 
Optical Flow by Deqing SunCodeOptical Flowhttp://www.cs.brown.edu/~dqsun/code/flow_code.zipD. Sun, S. Roth, M. J. Black, Secrets of Optical Flow Estimation and Their Principles, CVPR, 2010 
Horn and Schunck's Optical FlowCodeOptical Flowhttp://www.cs.brown.edu/~dqsun/code/hs.zip 
Dense Point TrackingCodeOptical Flowhttp://lmb.informatik.uni-freiburg.de/resources/binaries/N. Sundaram, T. Brox, K. Keutzer 
Large Displacement Optical FlowCodeOptical Flowhttp://lmb.informatik.uni-freiburg.de/resources/binaries/T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, PAMI 2011 
Classical Variational Optical FlowCodeOptical Flowhttp://lmb.informatik.uni-freiburg.de/resources/binaries/T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, ECCV 2004 
Optimization Algorithms in Machine LearningTalkOptimizationhttp://videolectures.net/nips2010_wright_oaml/Stephen J. Wright, Computer Sciences Department, University of Wisconsin - Madison 
Convex OptimizationTalkOptimizationhttp://videolectures.net/mlss2011_vandenberghe_convex/Lieven Vandenberghe, Electrical Engineering Department, University of California, Los Angeles 
Energy Minimization with Label costs and Applications in Multi-Model FittingTalkOptimizationhttp://videolectures.net/nipsworkshops2010_boykov_eml/Yuri Boykov, Department of Computer Science, University of Western Ontario 
Who is Afraid of Non-Convex Loss Functions?TalkOptimizationhttp://videolectures.net/eml07_lecun_wia/Yann LeCun, New York University 
Optimization Algorithms in Support Vector MachinesTalkOptimization and Support Vector Machineshttp://videolectures.net/mlss09us_wright_oasvm/Stephen J. Wright, Computer Sciences Department, University of Wisconsin - Madison 
Training Deformable Models for LocalizationCodePose Estimationhttp://www.ics.uci.edu/~dramanan/papers/parse/index.htmlRamanan, D. "Learning to Parse Images of Articulated Bodies." NIPS 2006 
Articulated Pose Estimation using Flexible Mixtures of PartsCodePose Estimationhttp://phoenix.ics.uci.edu/software/pose/Y. Yang, D. Ramanan, Articulated Pose Estimation using Flexible Mixtures of Parts, CVPR 2011 
Calvin Upper-Body DetectorCodePose Estimationhttp://www.vision.ee.ethz.ch/~calvin/calvin_upperbody_detector/E. Marcin,  F. Vittorio, Better Appearance Models for Pictorial Structures, BMVC 2009 
Estimating Human Pose from Occluded ImagesCodePose Estimationhttp://faculty.ucmerced.edu/mhyang/code/accv09_pose.zipJ.-B. Huang and M.-H. Yang, Estimating Human Pose from Occluded Images, ACCV 2009 
Relative EntropyTalkRelative Entropyhttp://videolectures.net/nips09_verdu_re/Sergio Verdu, Princeton University 
Saliency-based video segmentationCodeSaliency Detectionhttp://www.brl.ntt.co.jp/people/akisato/saliency3.htmlK. Fukuchi, K.  Miyazato, A. Kimura, S. Takagi and J. Yamato, Saliency-based video segmentation with graph cuts and sequentially updated priors, ICME 2009 
Saliency Using Natural statisticsCodeSaliency Detectionhttp://cseweb.ucsd.edu/~l6zhang/L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 2008 
Context-aware saliency detectionCodeSaliency Detectionhttp://webee.technion.ac.il/labs/cgm/Computer-Graphics-Multimedia/Software/Saliency/Saliency.htmlS. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, 2010. 
Learning to Predict Where Humans LookCodeSaliency Detectionhttp://people.csail.mit.edu/tjudd/WherePeopleLook/index.htmlT. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look, ICCV, 2009 
Graph-based visual saliencyCodeSaliency Detectionhttp://www.klab.caltech.edu/~harel/share/gbvs.phpJ. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007 
Discriminant Saliency for Visual Recognition from Cluttered ScenesCodeSaliency Detectionhttp://www.svcl.ucsd.edu/projects/saliency/D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes, NIPS, 2004 
Global Contrast based Salient Region DetectionCodeSaliency Detectionhttp://cg.cs.tsinghua.edu.cn/people/~cmm/saliency/M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection. CVPR, 2011 
Itti, Koch, and Niebur' saliency detectionCodeSaliency Detectionhttp://www.saliencytoolbox.net/L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 1998 
Learning Hierarchical Image Representation with Sparsity, Saliency and LocalityCodeSaliency Detection J. Yang and M.-H. Yang, Learning Hierarchical Image Representation with Sparsity, Saliency and Locality, BMVC 2011 
Spectrum Scale Space based Visual SaliencyCodeSaliency Detectionhttp://www.cim.mcgill.ca/~lijian/saliency.htmJ Li, M D. Levine, X An and H. He, Saliency Detection Based on Frequency and Spatial Domain Analyses, BMVC 2011 
Attention via Information MaximizationCodeSaliency Detectionhttp://www.cse.yorku.ca/~neil/AIM.zipN. Bruce and J. Tsotsos. Saliency based on information maximization. In NIPS, 2005 
Saliency detection: A spectral residual approachCodeSaliency Detectionhttp://www.klab.caltech.edu/~xhou/projects/spectralResidual/spectralresidual.htmlX. Hou and L. Zhang. Saliency detection: A spectral residual approach. CVPR, 2007 
Saliency detection using maximum symmetric surroundCodeSaliency Detectionhttp://ivrg.epfl.ch/supplementary_material/RK_ICIP2010/index.htmlR. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 2010 
Frequency-tuned salient region detectionCodeSaliency Detectionhttp://ivrgwww.epfl.ch/supplementary_material/RK_CVPR09/index.htmlR. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009 
Segmenting salient objects from images and videosCodeSaliency Detectionhttp://www.cse.oulu.fi/MVG/Downloads/saliencyE. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos. CVPR, 2010 
Diffusion Geometry Methods in Shape AnalysisTutorialShape Analysis, Diffusion Geometryhttp://tosca.cs.technion.ac.il/book/course_eccv10.htmlA. Brontein and M. Bronstein, ECCV 2010 Tutorial 
Source Code Collection for Reproducible ResearchLinkSource codehttp://www.csee.wvu.edu/~xinl/reproducible_research.htmlcollected by Xin Li, Lane Dept of CSEE, West Virginia University 
Computer Vision Algorithm ImplementationsLinkSource codehttp://www.cvpapers.com/rr.htmlCVPapers 
Robust Sparse Coding for Face RecognitionCodeSparse Representationhttp://www4.comp.polyu.edu.hk/~cslzhang/code/RSC.zipM. Yang, L. Zhang, J. Yang and D. Zhang, “Robust Sparse Coding for Face Recognition,” CVPR 2011 
Sparse coding simulation softwareCodeSparse Representationhttp://redwood.berkeley.edu/bruno/sparsenet/Olshausen BA, Field DJ, "Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images", Nature 1996 
Sparse and Redundant Representations: From Theory to Applications in Signal and Image ProcessingCodeSparse Representationhttp://www.cs.technion.ac.il/~elad/Various/Matlab-Package-Book.rarM. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing 
Fisher Discrimination Dictionary Learning for Sparse RepresentationCodeSparse Representationhttp://www4.comp.polyu.edu.hk/~cslzhang/code/FDDL.zipM. Yang, L. Zhang, X. Feng and D. Zhang, Fisher Discrimination Dictionary Learning for Sparse Representation, ICCV 2011 
Efficient sparse coding algorithmsCodeSparse Representationhttp://ai.stanford.edu/~hllee/softwares/nips06-sparsecoding.htmH. Lee, A. Battle, R. Rajat and A. Y. Ng, Efficient sparse coding algorithms, NIPS 2007 
A Linear Subspace Learning Approach via Sparse CodingCodeSparse Representationhttp://www4.comp.polyu.edu.hk/~cslzhang/code/LSL_SC.zipL. Zhang, P. Zhu, Q. Hu and D. Zhang, “A Linear Subspace Learning Approach via Sparse Coding,” ICCV 2011 
SPArse Modeling SoftwareCodeSparse Representationhttp://www.di.ens.fr/willow/SPAMS/J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online Learning for Matrix Factorization and Sparse Coding, JMLR 2010 
Sparse Methods for Machine Learning: Theory and AlgorithmsTalkSparse Representationhttp://videolectures.net/nips09_bach_smm/Francis R. Bach, INRIA 
Centralized Sparse Representation for Image RestorationCodeSparse Representationhttp://www4.comp.polyu.edu.hk/~cslzhang/code/CSR_IR.zipW. Dong, L. Zhang and G. Shi, “Centralized Sparse Representation for Image Restoration,” ICCV 2011 
A Tutorial on Spectral ClusteringTutorialSpectral Clusteringhttp://web.mit.edu/~wingated/www/introductions/tutorial_on_spectral_clustering.pdfUlrike von Luxburg, Max Planck Institute for Biological Cybernetics 
Statistical Learning TheoryTalkStatistical Learning Theoryhttp://videolectures.net/mlss04_taylor_slt/John Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College London 
Stereo EvaluationCodeStereohttp://vision.middlebury.edu/stereo/D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV 2001 
Constant-Space Belief PropagationCodeStereohttp://www.cs.cityu.edu.hk/~qiyang/publications/code/cvpr-10-csbp/csbp.htmQ. Yang, L. Wang, and N. Ahuja, A Constant-Space Belief Propagation Algorithm for Stereo Matching, CVPR 2010 
libmvCodeStructure from motionhttp://code.google.com/p/libmv/ 
Structure from Motion toolbox for Matlab by Vincent RabaudCodeStructure from motionhttp://code.google.com/p/vincents-structure-from-motion-matlab-toolbox/ 
FIT3DCodeStructure from motionhttp://www.fit3d.info/ 
VisualSFM : A Visual Structure from Motion SystemCodeStructure from motionhttp://www.cs.washington.edu/homes/ccwu/vsfm/ 
Structure and Motion Toolkit in MatlabCodeStructure from motionhttp://cms.brookes.ac.uk/staff/PhilipTorr/Code/code_page_4.htm 
Nonrigid Structure from MotionTutorialStructure from motionhttp://www.cs.cmu.edu/~yaser/ECCV2010Tutorial.htmlY. Sheikh and Sohaib Khan, ECCV 2010 Tutorial 
BundlerCodeStructure from motionhttp://phototour.cs.washington.edu/bundler/N. Snavely, S M. Seitz, R Szeliski. Photo Tourism: Exploring image collections in 3D. SIGGRAPH 2006 
Nonrigid Structure From Motion in Trajectory SpaceCodeStructure from motionhttp://cvlab.lums.edu.pk/nrsfm/index.html 
OpenSourcePhotogrammetryCodeStructure from motionhttp://opensourcephotogrammetry.blogspot.com/ 
Structured Prediction and Learning in Computer VisionTutorialStructured Predictionhttp://www.nowozin.net/sebastian/cvpr2011tutorial/S. Nowozin and C. Lampert, CVPR 2011 Tutorial 
Generalized Principal Component AnalysisCodeSubspace Learninghttp://www.vision.jhu.edu/downloads/main.php?dlID=c1R. Vidal, Y. Ma and S. Sastry. Generalized Principal Component Analysis (GPCA), CVPR 2003 
Text recognition in the wildCodeText Recognitionhttp://vision.ucsd.edu/~kai/grocr/K. Wang, B. Babenko, and S. Belongie, End-to-end Scene Text Recognition, ICCV 2011 
Neocognitron for handwritten digit recognitionCodeText Recognitionhttp://visiome.neuroinf.jp/modules/xoonips/detail.php?item_id=375K. Fukushima: "Neocognitron for handwritten digit recognition", Neurocomputing, 2003 
Image Quilting for Texture Synthesis and TransferCodeTexture Synthesishttp://www.cs.cmu.edu/~efros/quilt_research_code.zipA. A. Efros and W. T. Freeman, Image Quilting for Texture Synthesis and Transfer, SIGGRAPH 2001 
Variational methods for computer visionTutorialVariational Calculushttp://cvpr.in.tum.de/tutorials/iccv2011Daniel Cremers, Bastian Goldlucke, Thomas Pock, ICCV 2011 Tutorial 
Variational Methods in Computer VisionTutorialVariational Calculushttp://cvpr.cs.tum.edu/tutorials/eccv2010D. Cremers, B. Goldlücke, T. Pock, ECCV 2010 Tutorial 
Understanding Visual ScenesTalkVisual Recognitionhttp://videolectures.net/nips09_torralba_uvs/Antonio Torralba, MIT 
Visual Recognition, University of Texas at Austin, Fall 2011CourseVisual Recognitionhttp://www.cs.utexas.edu/~grauman/courses/fall2011/schedule.htmlKristen Grauman 
Tracking using Pixel-Wise PosteriorsCodeVisual Trackinghttp://www.robots.ox.ac.uk/~cbibby/research_pwp.shtmlC. Bibby and I. Reid, Tracking using Pixel-Wise Posteriors, ECCV 2008 
Visual Tracking with Histograms and Articulating BlocksCodeVisual Trackinghttp://www.cise.ufl.edu/~smshahed/tracking.htmS. M. Shshed Nejhum, J.  Ho, and M.-H.Yang, Visual Tracking with Histograms and Articulating Blocks, CVPR 2008 
Lucas-Kanade affine template trackingCodeVisual Trackinghttp://www.mathworks.com/matlabcentral/fileexchange/24677-lucas-kanade-affine-template-trackingS. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, IJCV 2002 
Visual Tracking DecompositionCodeVisual Trackinghttp://cv.snu.ac.kr/research/~vtd/J Kwon and K. M. Lee, Visual Tracking Decomposition, CVPR 2010 
GPU Implementation of Kanade-Lucas-Tomasi Feature TrackerCodeVisual Trackinghttp://cs.unc.edu/~ssinha/Research/GPU_KLT/S. N Sinha, J.-M. Frahm, M. Pollefeys and Y. Genc, Feature Tracking and Matching in Video Using Programmable Graphics Hardware, MVA, 2007 
Motion Tracking in Image SequencesCodeVisual Trackinghttp://www.cs.berkeley.edu/~flw/tracker/C. Stauffer and W. E. L. Grimson. Learning patterns of activity using real-time tracking, PAMI, 2000 
Particle Filter Object TrackingCodeVisual Trackinghttp://blogs.oregonstate.edu/hess/code/particles/ 
Tracking with Online Multiple Instance LearningCodeVisual Trackinghttp://vision.ucsd.edu/~bbabenko/project_miltrack.shtmlB. Babenko, M.-H. Yang, S. Belongie, Visual Tracking with Online Multiple Instance Learning, PAMI 2011 
KLT: An Implementation of the Kanade-Lucas-Tomasi Feature TrackerCodeVisual Trackinghttp://www.ces.clemson.edu/~stb/klt/B. D. Lucas and T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. IJCAI, 1981 
Superpixel TrackingCodeVisual Trackinghttp://faculty.ucmerced.edu/mhyang/papers/iccv11a.htmlS. Wang, H. Lu, F. Yang, and M.-H. Yang, Superpixel Tracking, ICCV 2011 
L1 TrackingCodeVisual Trackinghttp://www.dabi.temple.edu/~hbling/code_data.htmX. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009 
Online Discriminative Object Tracking with Local Sparse RepresentationCodeVisual Trackinghttp://faculty.ucmerced.edu/mhyang/code/wacv12a_code.zipQ. Wang, F. Chen, W. Xu, and M.-H. Yang, Online Discriminative Object Tracking with Local Sparse Representation, WACV 2012 
Incremental Learning for Robust Visual TrackingCodeVisual Trackinghttp://www.cs.toronto.edu/~dross/ivt/D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007 
Online boosting trackersCodeVisual Trackinghttp://www.vision.ee.ethz.ch/boostingTrackers/H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR, 2006 
Globally-Optimal Greedy Algorithms for Tracking a Variable Number of ObjectsCodeVisual Trackinghttp://www.ics.uci.edu/~hpirsiav/papers/tracking_cvpr11_release_v1.0.tar.gzH. Pirsiavash, D. Ramanan, C. Fowlkes. "Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects, CVPR 2011 
Object TrackingCodeVisual Trackinghttp://plaza.ufl.edu/lvtaoran/object%20tracking.htm
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