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


     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 Future Talk 3D Computer Vision http://www.youtube.com/watch?v=kyIzMr917Rc Steven Seitz, University of Washington, Google Tech Talk, 2011                                      
Computer Vision and 3D Perception for Robotics Tutorial 3D perception http://www.willowgarage.com/workshops/2010/eccv Radu 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) Tutorial 3D point cloud processing http://www.pointclouds.org/media/iccv2011.html R. Rusu, S. Holzer, M. Dixon, V. Rabaud, ICCV 2011 Tutorial  
Looking at people: The past, the present and the future Tutorial Action Recognition http://www.cs.brown.edu/~ls/iccv2011tutorial.html L. Sigal, T. Moeslund, A. Hilton, V. Kruger, ICCV 2011 Tutorial  
Frontiers of Human Activity Analysis Tutorial Action Recognition http://cvrc.ece.utexas.edu/mryoo/cvpr2011tutorial/ J. K. Aggarwal, Michael S. Ryoo, and Kris Kitani, CVPR 2011 Tutorial  
Statistical and Structural Recognition of Human Actions Tutorial Action Recognition https://sites.google.com/site/humanactionstutorialeccv10/ Ivan Laptev and Greg Mori, ECCV 2010 Tutorial  
Dense Trajectories Video Description Code Action Recognition http://lear.inrialpes.fr/people/wang/dense_trajectories H. Wang and A. Klaser and C. Schmid and C.- L. Liu, Action Recognition by Dense Trajectories, CVPR, 2011  
3D Gradients (HOG3D) Code Action Recognition http://lear.inrialpes.fr/people/klaeser/research_hog3d A. Klaser, M. Marszałek, and C. Schmid, BMVC, 2008.  
Spectral Matting Code Alpha Matting http://www.vision.huji.ac.il/SpectralMatting/ A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008  
Learning-based Matting Code Alpha Matting http://www.mathworks.com/matlabcentral/fileexchange/31412 Y. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009  
Bayesian Matting Code Alpha Matting http://www1.idc.ac.il/toky/CompPhoto-09/Projects/Stud_projects/Miki/index.html Y. Y. Chuang, B. Curless, D. H. Salesin, and R. Szeliski, A Bayesian Approach to Digital Matting, CVPR, 2001  
Closed Form Matting Code Alpha Matting http://people.csail.mit.edu/alevin/matting.tar.gz A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting, PAMI 2008.  
Shared Matting Code Alpha Matting http://www.inf.ufrgs.br/~eslgastal/SharedMatting/ E. S. L. Gastal and M. M. Oliveira, Computer Graphics Forum, 2010  
Introduction To Bayesian Inference Talk Bayesian Inference http://videolectures.net/mlss09uk_bishop_ibi/ Christopher Bishop, Microsoft Research  
Modern Bayesian Nonparametrics Talk Bayesian Nonparametrics http://www.youtube.com/watch?v=F0_ih7THV94&feature=relmfu Peter Orbanz and Yee Whye Teh  
Theory and Applications of Boosting Talk Boosting http://videolectures.net/mlss09us_schapire_tab/ Robert Schapire, Department of Computer Science, Princeton University  
Epipolar Geometry Toolbox Code Camera Calibration http://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 Matlab Code Camera Calibration http://www.vision.caltech.edu/bouguetj/calib_doc/ http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/ref.html  
EasyCamCalib Code Camera Calibration http://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 Project Code Clustering http://vision.ucsd.edu/~sagarwal/spectral-0.2.tgz  
K-Means - Oxford Code Code Clustering http://www.cs.ucf.edu/~vision/Code/vggkmeans.zip  
Self-Tuning Spectral Clustering Code Clustering http://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.html  
K-Means - VLFeat Code Clustering http://www.vlfeat.org/  
Spectral Clustering - UW Project Code Clustering http://www.stat.washington.edu/spectral/  
Color image understanding: from acquisition to high-level image understanding Tutorial Color Image Processing http://www.cat.uab.cat/~joost/tutorial_iccv.html Theo Gevers, Keigo Hirakawa, Joost van de Weijer, ICCV 2011 Tutorial  
Sketching the Common Code Common Visual Pattern Discovery http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/SketchCommonCVPR10_v1.1.tar.gz S. Bagon, O. Brostovsky, M. Galun and M. Irani, Detecting and Sketching the Common, CVPR 2010  
Common Visual Pattern Discovery via Spatially Coherent Correspondences Code Common Visual Pattern Discovery https://sites.google.com/site/lhrbss/home/papers/SimplifiedCode.zip?attredirects=0 H. Liu, S. Yan, "Common Visual Pattern Discovery via Spatially Coherent Correspondences", CVPR 2010  
Fcam: an architecture and API for computational cameras Tutorial Computational Imaging http://fcam.garage.maemo.org/iccv2011.html Kari Pulli, Andrew Adams, Timo Ahonen, Marius Tico, ICCV 2011 Tutorial  
Computational Photography, University of Illinois, Urbana-Champaign, Fall 2011 Course Computational Photography http://www.cs.illinois.edu/class/fa11/cs498dh/ Derek Hoiem  
Computational Photography, CMU, Fall 2011 Course Computational Photography http://graphics.cs.cmu.edu/courses/15-463/2011_fall/463.html Alexei “Alyosha” Efros  
Computational Symmetry: Past, Current, Future Tutorial Computational Symmetry http://vision.cse.psu.edu/research/symmComp/index.shtml Yanxi Liu, ECCV 2010 Tutorial  
Introduction to Computer Vision, Stanford University, Winter 2010-2011 Course Computer Vision http://vision.stanford.edu/teaching/cs223b/ Fei-Fei Li  
Computer Vision: From 3D Reconstruction to Visual Recognition, Fall 2012 Course Computer Vision https://www.coursera.org/course/computervision Silvio Savarese and Fei-Fei Li  
Computer Vision, University of Texas at Austin, Spring 2011 Course Computer Vision http://www.cs.utexas.edu/~grauman/courses/spring2011/index.html Kristen Grauman  
Learning-Based Methods in Vision, CMU, Spring 2012 Course Computer Vision https://docs.google.com/document/pub?id=1jGBn7zPDEaU33fJwi3YI_usWS-U6gpSSJotV_2gDrL0 Alexei “Alyosha” Efros and Leonid Sigal  
Introduction to Computer Vision Course Computer Vision http://www.cs.brown.edu/courses/cs143/ James Hays, Brown University, Fall 2011  
Computer Image Analysis, Computer Vision Conferences Link Computer Vision http://iris.usc.edu/information/Iris-Conferences.html USC  
CV Papers on the web Link Computer Vision http://www.cvpapers.com/index.html CVPapers  
Computer Vision, University of North Carolina at Chapel Hill, Spring 2010 Course Computer Vision http://www.cs.unc.edu/~lazebnik/spring10/ Svetlana Lazebnik  
CVonline Link Computer Vision http://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 2012 Course Computer Vision https://www.coursera.org/course/vision Jitendra Malik  
Computer Vision, New York University, Fall 2012 Course Computer Vision http://cs.nyu.edu/~fergus/teaching/vision_2012/index.html Rob Fergus  
Advances in Computer Vision Course Computer Vision http://groups.csail.mit.edu/vision/courses/6.869/ Antonio Torralba, MIT, Spring 2010  
Annotated Computer Vision Bibliography Link Computer Vision http://iris.usc.edu/Vision-Notes/bibliography/contents.html compiled by Keith Price  
Computer Vision, University of Illinois, Urbana-Champaign, Spring 2012 Course Computer Vision http://www.cs.illinois.edu/class/sp12/cs543/ Derek Hoiem  
The Computer Vision homepage Link Computer Vision http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html  
Computer Vision, University of Washington, Winter 2012 Course Computer Vision http://www.cs.washington.edu/education/courses/cse455/12wi/ Steven Seitz  
CV Datasets on the web Link Computer Vision http://www.cvpapers.com/datasets.html CVPapers  
The Computer Vision Industry Link Computer Vision Industry http://www.cs.ubc.ca/~lowe/vision.html David Lowe  
Compiled list of recognition datasets Link Dataset http://www.cs.utexas.edu/~grauman/courses/spring2008/datasets.htm compiled by Kristen Grauman  
Decision forests for classification, regression, clustering and density estimation Tutorial Decision Forests http://research.microsoft.com/en-us/groups/vision/decisionforests.aspx A. Criminisi, J. Shotton and E. Konukoglu, ICCV 2011 Tutorial  
A tutorial on Deep Learning Talk Deep Learning http://videolectures.net/jul09_hinton_deeplearn/ Geoffrey E. Hinton, Department of Computer Science, University of Toronto  
Kernel Density Estimation Toolbox Code Density Estimation http://www.ics.uci.edu/~ihler/code/kde.html  
Kinect SDK Code Depth Sensor http://www.microsoft.com/en-us/kinectforwindows/ http://www.microsoft.com/en-us/kinectforwindows/  
LLE Code Dimension Reduction http://www.cs.nyu.edu/~roweis/lle/code.html  
Laplacian Eigenmaps Code Dimension Reduction http://www.cse.ohio-state.edu/~mbelkin/algorithms/Laplacian.tar  
Diffusion maps Code Dimension Reduction http://www.stat.cmu.edu/~annlee/software.htm  
ISOMAP Code Dimension Reduction http://isomap.stanford.edu/  
Dimensionality Reduction Toolbox Code Dimension Reduction http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html  
Matlab Toolkit for Distance Metric Learning Code Distance Metric Learning http://www.cs.cmu.edu/~liuy/distlearn.htm  
Distance Functions and Metric Learning Tutorial Distance Metric Learning http://www.cs.huji.ac.il/~ofirpele/DFML_ECCV2010_tutorial/ M. Werman, O. Pele and  B. Kulis, ECCV 2010 Tutorial  
Distance Transforms of Sampled Functions Code Distance Transformation http://people.cs.uchicago.edu/~pff/dt/  
Hidden Markov Models Tutorial Expectation Maximization http://crow.ee.washington.edu/people/bulyko/papers/em.pdf Jeff A. Bilmes, University of California at Berkeley  
Edge Foci Interest Points Code Feature Detection http://research.microsoft.com/en-us/um/people/larryz/edgefoci/edge_foci.htm L. Zitnickand K. Ramnath, Edge Foci Interest Points, ICCV, 2011  
Boundary Preserving Dense Local Regions Code Feature Detection http://vision.cs.utexas.edu/projects/bplr/bplr.html J. Kim and K. Grauman, Boundary Preserving Dense Local Regions, CVPR 2011  
Canny Edge Detection Code Feature Detection http://www.mathworks.com/help/toolbox/images/ref/edge.html J. Canny, A Computational Approach To Edge Detection, PAMI, 1986  
FAST Corner Detection Code Feature Detection http://www.edwardrosten.com/work/fast.html E. Rosten and T. Drummond, Machine learning for high-speed corner detection, ECCV, 2006  
Groups of Adjacent Contour Segments Code Feature Detection; Feature Extraction http://www.robots.ox.ac.uk/~vgg/share/ferrari/release-kas-v102.tgz V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, Groups of Adjacent Contour Segments for Object Detection, PAMI, 2007  
Maximally stable extremal regions (MSER) - VLFeat Code Feature Detection; Feature Extraction http://www.vlfeat.org/ J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002  
Geometric Blur Code Feature Detection; Feature Extraction http://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-SIFT Code Feature Detection; Feature Extraction http://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 Software Code Feature Detection; Feature Extraction http://www.cs.ubc.ca/~lowe/keypoints/ D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.  
Affine Covariant Features Code Feature Detection; Feature Extraction http://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) - Library Code Feature Detection; Feature Extraction http://blogs.oregonstate.edu/hess/code/sift/ D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.  
Maximally stable extremal regions (MSER) Code Feature Detection; Feature Extraction http://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 Descriptor Code Feature Detection; Feature Extraction http://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 SURF Code Feature Detection; Feature Extraction http://www.chrisevansdev.com/computer-vision-opensurf.html H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006  
Scale-invariant feature transform (SIFT) - VLFeat Code Feature Detection; Feature Extraction http://www.vlfeat.org/ D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.  
Speeded Up Robust Feature (SURF) - Matlab Wrapper Code Feature Detection; Feature Extraction http://www.maths.lth.se/matematiklth/personal/petter/surfmex.php H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006  
Space-Time Interest Points (STIP) Code Feature Detection; Feature Extraction; Action Recognition http://www.irisa.fr/vista/Equipe/People/Laptev/download/stip-1.1-winlinux.zip; http://www.nada.kth.se/cvap/abstracts/cvap284.html I. Laptev, On Space-Time Interest Points, IJCV, 2005; I. Laptev and T. Lindeberg, On Space-Time Interest Points, IJCV 2005  
PCA-SIFT Code Feature Extraction http://www.cs.cmu.edu/~yke/pcasift/ Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004  
sRD-SIFT Code Feature Extraction http://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 Descriptor Code Feature Extraction http://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) Code Feature Extraction http://www.robots.ox.ac.uk/~vgg/research/caltech/phog/phog.zip A. Bosch, A. Zisserman, and X. Munoz, Representing shape with a spatial pyramid kernel, CIVR, 2007  
BRIEF: Binary Robust Independent Elementary Features Code Feature Extraction http://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-Similarity Code Feature Extraction http://www.vision.ee.ethz.ch/~calvin/gss/selfsim_release1.0.tgz T. 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 Descriptor Code Feature Extraction http://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 Context Code Feature Extraction http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sc_digits.html S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002  
Image and Video Description with Local Binary Pattern Variants Tutorial Feature Extraction http://www.ee.oulu.fi/research/imag/mvg/files/pdf/CVPR-tutorial-final.pdf M. Pietikainen and J. Heikkila, CVPR 2011 Tutorial  
Histogram of Oriented Graidents - OLT for windows Code Feature Extraction; Object Detection http://www.computing.edu.au/~12482661/hog.html N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005  
Histogram of Oriented Graidents - INRIA Object Localization Toolkit Code Feature Extraction; Object Detection http://www.navneetdalal.com/software N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005  
Feature Learning for Image Classification Tutorial Feature Learning, Image Classification http://ufldl.stanford.edu/eccv10-tutorial/ Kai Yu and Andrew Ng, ECCV 2010 Tutorial  
The Pyramid Match: Efficient Matching for Retrieval and Recognition Code Feature Matching; Image Classification http://www.cs.utexas.edu/~grauman/research/projects/pmk/pmk_projectpage.htm K. Grauman and T. Darrell.  The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005  
Game Theory in Computer Vision and Pattern Recognition Tutorial Game Theory http://www.dsi.unive.it/~atorsell/cvpr2011tutorial/ Marcello Pelillo and Andrea Torsello, CVPR 2011 Tutorial  
Gaussian Process Basics Talk Gaussian Process http://videolectures.net/gpip06_mackay_gpb/ David MacKay, University of Cambridge  
Hyper-graph Matching via Reweighted Random Walks Code Graph Matching http://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 Matching Code Graph Matching http://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 models Tutorial Graphical Models http://www.csd.uoc.gr/~komod/ICCV2011_tutorial/ Nikos Komodakis, Pawan Kumar, Nikos Paragios, Ramin Zabih, ICCV 2011 Tutorial  
Graphical Models and message-passing algorithms Talk Graphical Models http://videolectures.net/mlss2011_wainwright_messagepassing/ Martin J. Wainwright, University of California at Berkeley  
Graphical Models, Exponential Families, and Variational Inference Tutorial Graphical Models http://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf Martin J. Wainwright and Michael I. Jordan, University of California at Berkeley  
Inference in Graphical Models, Stanford University, Spring 2012 Course Graphical Models http://www.stanford.edu/~montanar/TEACHING/Stat375/stat375.html Andrea Montanari, Stanford University  
Ground shadow detection Code Illumination, Reflectance, and Shadow http://www.jflalonde.org/software.html#shadowDetection J.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs, ECCV 2010  
Estimating Natural Illumination from a Single Outdoor Image Code Illumination, Reflectance, and Shadow http://www.cs.cmu.edu/~jlalonde/software.html#skyModel J-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? Code Illumination, Reflectance, and Shadow http://www.cs.cmu.edu/~jlalonde/software.html#skyModel J-F. Lalonde, S. G. Narasimhan, A. A. Efros,  What Does the Sky Tell Us About the Camera?, ECCV 2008  
Shadow Detection using Paired Region Code Illumination, Reflectance, and Shadow http://www.cs.illinois.edu/homes/guo29/projects/shadow.html R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011  
Real-time Specular Highlight Removal Code Illumination, Reflectance, and Shadow http://www.cs.cityu.edu.hk/~qiyang/publications/code/eccv-10.zip Q. 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 Sequences Code Illumination, Reflectance, and Shadow http://www.cs.cmu.edu/~jlalonde/software.html#skyModel J-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 Classification Code Image Classification http://www.ifp.illinois.edu/~jyang29/ScSPM.htm J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification, CVPR, 2009  
Texture Classification Code Image Classification http://www.robots.ox.ac.uk/~vgg/research/texclass/index.html M. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005  
Locality-constrained Linear Coding Code Image Classification http://www.ifp.illinois.edu/~jyang29/LLC.htm J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification, CVPR, 2010  
Spatial Pyramid Matching Code Image Classification http://www.cs.unc.edu/~lazebnik/research/SpatialPyramid.zip S. 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 estimation Code Image Deblurring http://www.gris.tu-darmstadt.de/research/visinf/software/index.en.htm U. Schmidt, K. Schelten, and S. Roth. Bayesian deblurring with integrated noise estimation, CVPR 2011  
Richardson-Lucy Deblurring for Scenes under Projective Motion Path Code Image Deblurring http://yuwing.kaist.ac.kr/projects/projectivedeblur/projectivedeblur_files/ProjectiveDeblur.zip Y.-W. Tai, P. Tan, M. S. Brown: Richardson-Lucy Deblurring for Scenes under Projective Motion Path, PAMI 2011  
Analyzing spatially varying blur Code Image Deblurring http://www.eecs.harvard.edu/~ayanc/svblur/ A. Chakrabarti, T. Zickler, and W. T. Freeman, Analyzing Spatially-varying Blur, CVPR 2010  
Radon Transform Code Image Deblurring http://people.csail.mit.edu/taegsang/Documents/RadonDeblurringCode.zip T. 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 Deconvolution Code Image Deblurring http://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR2011Code.zip A. Levin, Y. Weiss, F. Durand, W. T. Freeman. Efficient Marginal Likelihood Optimization in Blind Deconvolution, CVPR 2011  
BLS-GSM Code Image Denoising http://decsai.ugr.es/~javier/denoise/  
Gaussian Field of Experts Code Image Denoising http://www.cs.huji.ac.il/~yweiss/BRFOE.zip  
Field of Experts Code Image Denoising http://www.cs.brown.edu/~roth/research/software.html  
BM3D Code Image Denoising http://www.cs.tut.fi/~foi/GCF-BM3D/  
Nonlocal means with cluster trees Code Image Denoising http://lmb.informatik.uni-freiburg.de/resources/binaries/nlmeans_brox_tip08Linux64.zip T. Brox, O. Kleinschmidt, D. Cremers, Efficient nonlocal means for denoising of textural patterns, TIP 2008  
Non-local Means Code Image Denoising http://dmi.uib.es/~abuades/codis/NLmeansfilter.m  
K-SVD Code Image Denoising http://www.cs.technion.ac.il/~ronrubin/Software/ksvdbox13.zip  
What makes a good model of natural images ? Code Image Denoising http://www.cs.huji.ac.il/~yweiss/BRFOE.zip Y. Weiss and W. T. Freeman, CVPR 2007  
Clustering-based Denoising Code Image Denoising http://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 Denoising Code Image Denoising http://www.csee.wvu.edu/~xinl/CSR.html W. Dong, X. Li, L. Zhang and G. Shi, Sparsity-based Image Denoising vis Dictionary Learning and Structural Clustering, CVPR, 2011  
Kernel Regressions Code Image Denoising http://www.soe.ucsc.edu/~htakeda/MatlabApp/KernelRegressionBasedImageProcessingToolBox_ver1-1beta.zip  
Learning Models of Natural Image Patches Code Image Denoising; Image Super-resolution; Image Deblurring http://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 Vision Code Image Denoising; Stereo Matching http://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 Filtering Code Image Filtering http://vision.ai.uiuc.edu/~qyang6/publications/code/cvpr-10-svmbf/program_video_conferencing.zip Q. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering,  
Local Laplacian Filters Code Image Filtering http://people.csail.mit.edu/sparis/publi/2011/siggraph/matlab_source_code.zip S. Paris, S. Hasinoff, J. Kautz, Local Laplacian Filters: Edge-Aware Image Processing with a Laplacian Pyramid, SIGGRAPH 2011  
Real-time O(1) Bilateral Filtering Code Image Filtering http://vision.ai.uiuc.edu/~qyang6/publications/code/qx_constant_time_bilateral_filter_ss.zip Q. Yang, K.-H. Tan and N. Ahuja,  Real-time O(1) Bilateral Filtering,  
Image smoothing via L0 Gradient Minimization Code Image Filtering http://www.cse.cuhk.edu.hk/~leojia/projects/L0smoothing/L0smoothing.zip L. Xu, C. Lu, Y. Xu, J. Jia, Image smoothing via L0 Gradient Minimization, SIGGRAPH Asia 2011  
Anisotropic Diffusion Code Image Filtering http://www.mathworks.com/matlabcentral/fileexchange/14995-anisotropic-diffusion-perona-malik P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, PAMI 1990  
Guided Image Filtering Code Image Filtering http://personal.ie.cuhk.edu.hk/~hkm007/eccv10/guided-filter-code-v1.rar K. He, J. Sun, X. Tang, Guided Image Filtering, ECCV 2010  
Fast Bilateral Filter Code Image Filtering http://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  
GradientShop Code Image Filtering http://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 Transformation Code Image Filtering http://inf.ufrgs.br/~eslgastal/DomainTransform/DomainTransformFilters-Source-v1.0.zip E. Gastal, M. Oliveira, Domain Transform for Edge-Aware Image and Video Processing, SIGGRAPH 2011  
Weighted Least Squares Filter Code Image Filtering http://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 Toolbox Code Image Processing; Image Filtering http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html Piotr Dollar, Piotr's Image & Video Matlab Toolbox, http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html  
Structural SIMilarity Code Image Quality Assessment https://ece.uwaterloo.ca/~z70wang/research/ssim/  
SPIQA Code Image Quality Assessment http://vision.ai.uiuc.edu/~bghanem2/shared_code/SPIQA_code.zip  
Feature SIMilarity Index Code Image Quality Assessment http://www4.comp.polyu.edu.hk/~cslzhang/IQA/FSIM/FSIM.htm  
Degradation Model Code Image Quality Assessment http://users.ece.utexas.edu/~bevans/papers/2000/imageQuality/index.html  
Tools and Methods for Image Registration Tutorial Image Registration http://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 Superpixels Code Image Segmentation http://ivrg.epfl.ch/supplementary_material/RK_SLICSuperpixels/index.html R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010  
Recovering Occlusion Boundaries from a Single Image Code Image Segmentation http://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 Tree Code Image Segmentation http://vision.ai.uiuc.edu/segmentation E. 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-Shift Code Image Segmentation http://www.vlfeat.org/overview/quickshift.html A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking, ECCV, 2008  
Efficient Graph-based Image Segmentation - C++ code Code Image Segmentation http://people.cs.uchicago.edu/~pff/segment/ P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004  
Turbepixels Code Image Segmentation http://www.cs.toronto.edu/~babalex/research.html A. 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 Mori Code Image Segmentation http://www.cs.sfu.ca/~mori/research/superpixels/ X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003  
Normalized Cut Code Image Segmentation http://www.cis.upenn.edu/~jshi/software/ J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000  
Mean-Shift Image Segmentation - Matlab Wrapper Code Image Segmentation http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/edison_matlab_interface.tar.gz D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002  
Segmenting Scenes by Matching Image Composites Code Image Segmentation http://www.cs.washington.edu/homes/bcr/projects/SceneComposites/index.html B. Russell, A. A. Efros, J.  Sivic, W. T. Freeman, A. Zisserman, NIPS 2009  
OWT-UCM Hierarchical Segmentation Code Image Segmentation http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011  
Entropy Rate Superpixel Segmentation Code Image Segmentation http://www.umiacs.umd.edu/~mingyliu/src/ers_matlab_wrapper_v0.1.zip M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011  
Efficient Graph-based Image Segmentation - Matlab Wrapper Code Image Segmentation http://www.mathworks.com/matlabcentral/fileexchange/25866-efficient-graph-based-image-segmentation P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004  
Biased Normalized Cut Code Image Segmentation http://www.cs.berkeley.edu/~smaji/projects/biasedNcuts/ S. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut, CVPR 2011  
Segmentation by Minimum Code Length Code Image Segmentation http://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 - EDISON Code Image Segmentation http://coewww.rutgers.edu/riul/research/code/EDISON/index.html D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002  
Self-Similarities for Single Frame Super-Resolution Code Image Super-resolution https://eng.ucmerced.edu/people/cyang35/ACCV10.zip C.-Y. Yang, J.-B. Huang, and M.-H. Yang, Exploiting Self-Similarities for Single Frame Super-Resolution, ACCV 2010  
MRF for image super-resolution Code Image Super-resolution http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.html W. T Freeman and C. Liu. Markov Random Fields for Super-resolution and Texture Synthesis. In A. Blake, P. Kohli, and C. Rother, eds., Advances in Markov Random Fields for Vision and Image Processing, Chapter 10. MIT Press, 2011
Sprarse coding super-resolution Code Image Super-resolution http://www.ifp.illinois.edu/~jyang29/ScSR.htm J. Yang, J. Wright, T. S. Huang, and Y. Ma. Image super-resolution via sparse representation, TIP 2010  
Multi-frame image super-resolution Code Image Super-resolution http://www.robots.ox.ac.uk/~vgg/software/SR/index.html Pickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis  
Single-Image Super-Resolution Matlab Package Code Image Super-resolution http://www.cs.technion.ac.il/~elad/Various/Single_Image_SR.zip R. Zeyde, M. Elad, and M. Protter, On Single Image Scale-Up using Sparse-Representations, LNCS 2010  
MDSP Resolution Enhancement Software Code Image Super-resolution http://users.soe.ucsc.edu/~milanfar/software/superresolution.html S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Fast and Robust Multi-frame Super-resolution, TIP 2004  
Nonparametric Scene Parsing via Label Transfer Code Image Understanding http://people.csail.mit.edu/celiu/LabelTransfer/index.html C. Liu, J. Yuen, and Antonio Torralba, Nonparametric Scene Parsing via Label Transfer, PAMI 2011  
Discriminative Models for Multi-Class Object Layout Code Image Understanding http://www.ics.uci.edu/~desaic/multiobject_context.zip C. Desai, D. Ramanan, C. Fowlkes. "Discriminative Models for Multi-Class Object Layout, IJCV 2011  
Towards Total Scene Understanding Code Image Understanding http://vision.stanford.edu/projects/totalscene/index.html L.-J. Li, R. Socher and Li F.-F.. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework, CVPR 2009  
Object Bank Code Image Understanding http://vision.stanford.edu/projects/objectbank/index.html Li-Jia Li, Hao Su, Eric P. Xing and Li Fei-Fei. Object Bank: A High-Level Image Representation for Scene Classification and Semantic Feature Sparsification, NIPS 2010  
SuperParsing Code Image Understanding http://www.cs.unc.edu/~jtighe/Papers/ECCV10/eccv10-jtighe-code.zip J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image  
Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics Code Image Understanding http://www.cs.cmu.edu/~abhinavg/blocksworld/#downloads A. Gupta, A. A. Efros, M. Hebert, Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics, ECCV 2010  
Information Theory Talk Information Theory http://videolectures.net/mlss09uk_mackay_it/ David MacKay, University of Cambridge  
Information Theory in Learning and Control Talk Information Theory http://www.youtube.com/watch?v=GKm53xGbAOk&feature=relmfu Naftali (Tali) Tishby, The Hebrew University  
Efficient Earth Mover's Distance with L1 Ground Distance (EMD_L1) Code Kernels and Distances http://www.dabi.temple.edu/~hbling/code/EmdL1_v3.zip H. Ling and K. Okada, An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison, PAMI 2007  
Machine learning and kernel methods for computer vision Talk Kernels and Distances http://videolectures.net/etvc08_bach_mlakm/ Francis R. Bach, INRIA  
Diffusion-based distance Code Kernels and Distances http://www.dabi.temple.edu/~hbling/code/DD_v1.zip H. Ling and K. Okada, Diffusion Distance for Histogram Comparison, CVPR 2006  
Fast Directional Chamfer Matching Code Kernels and Distances http://www.umiacs.umd.edu/~mingyliu/src/fdcm_matlab_wrapper_v0.2.zip  
Learning and Inference in Low-Level Vision Talk Low-level vision http://videolectures.net/nips09_weiss_lil/ Yair Weiss, School of Computer Science and Engineering, The Hebrew University of Jerusalem  
TILT: Transform Invariant Low-rank Textures Code Low-Rank Modeling http://perception.csl.uiuc.edu/matrix-rank/tilt.html Z. Zhang, A. Ganesh, X. Liang, and Y. Ma, TILT: Transform Invariant Low-rank Textures, IJCV 2011  
Low-Rank Matrix Recovery and Completion Code Low-Rank Modeling http://perception.csl.uiuc.edu/matrix-rank/sample_code.html  
RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition Code Low-Rank Modeling http://perception.csl.uiuc.edu/matrix-rank/rasl.html Y. Peng, A. Ganesh, J. Wright, W. Xu, and Y. Ma, RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition, CVPR 2010  
Statistical Pattern Recognition Toolbox Code Machine Learning http://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 MATLAB Code Machine Learning http://research.ics.tkk.fi/ica/fastica/ http://research.ics.tkk.fi/ica/book/  
Boosting Resources by Liangliang Cao Code Machine Learning http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm  
Netlab Neural Network Software Code Machine Learning http://www1.aston.ac.uk/eas/research/groups/ncrg/resources/netlab/ C. M. Bishop, Neural Networks for Pattern RecognitionㄝOxford University Press, 1995  
Matlab Tutorial Tutorial Matlab http://www.cs.unc.edu/~lazebnik/spring10/matlab.intro.html David Kriegman and Serge Belongie  
Writing Fast MATLAB Code Tutorial Matlab http://www.mathworks.com/matlabcentral/fileexchange/5685 Pascal Getreuer, Yale University  
MRF Minimization Evaluation Code MRF Optimization http://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-cut Code MRF Optimization http://vision.csd.uwo.ca/code/maxflow-v3.01.zip Y. Boykov and V. Kolmogorov, An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, PAMI 2004  
Planar Graph Cut Code MRF Optimization http://vision.csd.uwo.ca/code/PlanarCut-v1.0.zip F. R. Schmidt, E. Toppe and D. Cremers, Efficient Planar Graph Cuts with Applications in Computer Vision, CVPR 2009  
Max-flow/min-cut for massive grids Code MRF Optimization http://vision.csd.uwo.ca/code/regionpushrelabel-v1.03.zip A. Delong and Y. Boykov, A Scalable Graph-Cut Algorithm for N-D Grids, CVPR 2008  
Multi-label optimization Code MRF Optimization http://vision.csd.uwo.ca/code/gco-v3.0.zip Y. Boykov, O. Verksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001  
Max-flow/min-cut for shape fitting Code MRF Optimization http://www.csd.uwo.ca/faculty/yuri/Implementations/TouchExpand.zip V. Lempitsky and Y. Boykov, Global Optimization for Shape Fitting, CVPR 2007  
MILIS Code Multiple Instance Learning   Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010  
MILES Code Multiple Instance Learning http://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  
MIForests Code Multiple Instance Learning http://www.ymer.org/amir/software/milforests/ C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010  
DD-SVM Code Multiple Instance Learning   Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 2004  
DOGMA Code Multiple Kernel Learning http://dogma.sourceforge.net/ F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010  
SHOGUN Code Multiple Kernel Learning http://www.shogun-toolbox.org/ S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning. JMLR, 2006  
SimpleMKL Code Multiple Kernel Learning http://asi.insa-rouen.fr/enseignants/~arakotom/code/mklindex.html A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl. JMRL, 2008  
OpenKernel.org Code Multiple Kernel Learning http://www.openkernel.org/ F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011  
Matlab Functions for Multiple View Geometry Code Multiple View Geometry http://www.robots.ox.ac.uk/~vgg/hzbook/code/  
for Computer Vision and Image Processing Code Multiple View Geometry http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.html P. 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 Software Code Multi-View Stereo http://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 Stereo Code Multi-View Stereo http://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 Evaluation Code Multi-View Stereo http://vision.middlebury.edu/mview/ S. Seitz et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006  
Spectral Hashing Code Nearest Neighbors Matching http://www.cs.huji.ac.il/~yweiss/SpectralHashing/ Y. Weiss, A. Torralba, R. Fergus, Spectral Hashing, NIPS 2008  
FLANN: Fast Library for Approximate Nearest Neighbors Code Nearest Neighbors Matching http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN  
ANN: Approximate Nearest Neighbor Searching Code Nearest Neighbors Matching http://www.cs.umd.edu/~mount/ANN/  
LDAHash: Binary Descriptors for Matching in Large Image Databases Code Nearest Neighbors Matching http://cvlab.epfl.ch/research/detect/ldahash/index.php C. Strecha, A. M. Bronstein, M. M. Bronstein and P. Fua. LDAHash: Improved matching with smaller descriptors, PAMI, 2011.  
Coherency Sensitive Hashing Code Nearest Neighbors Matching http://www.eng.tau.ac.il/~simonk/CSH/index.html S. Korman, S. Avidan, Coherency Sensitive Hashing, ICCV 2011  
Learning in Hierarchical Architectures: from Neuroscience to Derived Kernels Talk Neuroscience http://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 applications Tutorial Non-linear Least Squares http://cvlab.epfl.ch/~fua/courses/lsq/ Pascal Fua, Vincent Lepetit, ICCV 2011 Tutorial  
Non-rigid registration and reconstruction Tutorial Non-rigid registration http://www.isr.ist.utl.pt/~adb/tutorial/ Alessio Del Bue, Lourdes Agapito, Adrien Bartoli, ICCV 2011 Tutorial  
Geometry constrained parts based detection Tutorial Object Detection http://ci2cv.net/tutorials/iccv-2011/ Simon Lucey, Jason Saragih, ICCV 2011 Tutorial  
Max-Margin Hough Transform Code Object Detection http://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 regions Code Object Detection http://www.cs.berkeley.edu/~chunhui/publications/cvpr09_v2.zip C. Gu, J. J. Lim, P. Arbelaez, and J. Malik, CVPR 2009  
Poselet Code Object Detection http://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 boosting Code Object Detection http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html ICCV 2005 short courses on Recognizing and Learning Object Categories  
Feature Combination Code Object Detection http://www.vision.ee.ethz.ch/~pgehler/projects/iccv09/index.html P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009  
Hough Forests for Object Detection Code Object Detection http://www.vision.ee.ethz.ch/~gallju/projects/houghforest/index.html J. Gall and V. Lempitsky, Class-Specific Hough Forests for Object Detection, CVPR, 2009  
Cascade Object Detection with Deformable Part Models Code Object Detection http://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 Models Code Object Detection http://people.cs.uchicago.edu/~pff/latent/ P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan.  
A simple parts and structure object detector Code Object Detection http://people.csail.mit.edu/fergus/iccv2005/partsstructure.html ICCV 2005 short courses on Recognizing and Learning Object Categories  
Object Recognition with Deformable Models Talk Object Detection http://www.youtube.com/watch?v=_J_clwqQ4gI Pedro Felzenszwalb, Brown University  
Ensemble of Exemplar-SVMs for Object Detection and Beyond Code Object Detection http://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 Detection Code Object Detection http://pr.willowgarage.com/wiki/FaceDetection P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR, 2001  
Implicit Shape Model Code Object Detection http://www.vision.ee.ethz.ch/~bleibe/code/ism.html B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation, IJCV, 2008  
Multiple Kernels Code Object Detection http://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-SVMs Code Object Detection http://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 Collections Code Object Discovery http://people.csail.mit.edu/brussell/research/proj/mult_seg_discovery/index.html B. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, Using Multiple Segmentations to Discover Objects and their Extent in Image Collections, CVPR 2006  
Objectness measure Code Object Proposal http://www.vision.ee.ethz.ch/~calvin/objectness/objectness-release-v1.01.tar.gz B. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010  
Parametric min-cut Code Object Proposal http://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 Proposal Code Object Proposal http://vision.cs.uiuc.edu/proposals/ I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010  
Biologically motivated object recognition Code Object Recognition http://cbcl.mit.edu/software-datasets/standardmodel/index.html T. Serre, L. Wolf and T. Poggio. Object recognition with features inspired by visual cortex, CVPR 2005  
Recognition by Association via Learning Per-exemplar Distances Code Object Recognition http://www.cs.cmu.edu/~tmalisie/projects/cvpr08/dfuns.tar.gz T. Malisiewicz, A. A. Efros, Recognition by Association via Learning Per-exemplar Distances, CVPR 2008  
Sparse to Dense Labeling Code Object Segmentation http://lmb.informatik.uni-freiburg.de/resources/binaries/SparseToDenseLabeling.tar.gz P. Ochs, T. Brox, Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions, ICCV 2011  
ClassCut for Unsupervised Class Segmentation Code Object Segmentation http://www.vision.ee.ethz.ch/~calvin/classcut/ClassCut-release.zip B. Alexe, T. Deselaers and V. Ferrari, ClassCut for Unsupervised Class Segmentation, ECCV 2010  
Geodesic Star Convexity for Interactive Image Segmentation Code Object Segmentation http://www.robots.ox.ac.uk/~vgg/software/iseg/index.shtml V. Gulshan, C. Rother, A. Criminisi, A. Blake and A. Zisserman. Geodesic star convexity for interactive image segmentation  
Black and Anandan's Optical Flow Code Optical Flow http://www.cs.brown.edu/~dqsun/code/ba.zip  
Optical Flow Evaluation Code Optical Flow http://vision.middlebury.edu/flow/ S. Baker et al. A Database and Evaluation Methodology for Optical Flow, IJCV, 2011  
Optical Flow by Deqing Sun Code Optical Flow http://www.cs.brown.edu/~dqsun/code/flow_code.zip D. Sun, S. Roth, M. J. Black, Secrets of Optical Flow Estimation and Their Principles, CVPR, 2010  
Horn and Schunck's Optical Flow Code Optical Flow http://www.cs.brown.edu/~dqsun/code/hs.zip  
Dense Point Tracking Code Optical Flow http://lmb.informatik.uni-freiburg.de/resources/binaries/ N. Sundaram, T. Brox, K. Keutzer  
Large Displacement Optical Flow Code Optical Flow http://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 Flow Code Optical Flow http://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 Learning Talk Optimization http://videolectures.net/nips2010_wright_oaml/ Stephen J. Wright, Computer Sciences Department, University of Wisconsin - Madison  
Convex Optimization Talk Optimization http://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 Fitting Talk Optimization http://videolectures.net/nipsworkshops2010_boykov_eml/ Yuri Boykov, Department of Computer Science, University of Western Ontario  
Who is Afraid of Non-Convex Loss Functions? Talk Optimization http://videolectures.net/eml07_lecun_wia/ Yann LeCun, New York University  
Optimization Algorithms in Support Vector Machines Talk Optimization and Support Vector Machines http://videolectures.net/mlss09us_wright_oasvm/ Stephen J. Wright, Computer Sciences Department, University of Wisconsin - Madison  
Training Deformable Models for Localization Code Pose Estimation http://www.ics.uci.edu/~dramanan/papers/parse/index.html Ramanan, D. "Learning to Parse Images of Articulated Bodies." NIPS 2006  
Articulated Pose Estimation using Flexible Mixtures of Parts Code Pose Estimation http://phoenix.ics.uci.edu/software/pose/ Y. Yang, D. Ramanan, Articulated Pose Estimation using Flexible Mixtures of Parts, CVPR 2011  
Calvin Upper-Body Detector Code Pose Estimation http://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 Images Code Pose Estimation http://faculty.ucmerced.edu/mhyang/code/accv09_pose.zip J.-B. Huang and M.-H. Yang, Estimating Human Pose from Occluded Images, ACCV 2009  
Relative Entropy Talk Relative Entropy http://videolectures.net/nips09_verdu_re/ Sergio Verdu, Princeton University  
Saliency-based video segmentation Code Saliency Detection http://www.brl.ntt.co.jp/people/akisato/saliency3.html K. 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 statistics Code Saliency Detection http://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 detection Code Saliency Detection http://webee.technion.ac.il/labs/cgm/Computer-Graphics-Multimedia/Software/Saliency/Saliency.html S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, 2010.  
Learning to Predict Where Humans Look Code Saliency Detection http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look, ICCV, 2009  
Graph-based visual saliency Code Saliency Detection http://www.klab.caltech.edu/~harel/share/gbvs.php J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007  
Discriminant Saliency for Visual Recognition from Cluttered Scenes Code Saliency Detection http://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 Detection Code Saliency Detection http://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 detection Code Saliency Detection http://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 Locality Code Saliency Detection   J. Yang and M.-H. Yang, Learning Hierarchical Image Representation with Sparsity, Saliency and Locality, BMVC 2011  
Spectrum Scale Space based Visual Saliency Code Saliency Detection http://www.cim.mcgill.ca/~lijian/saliency.htm J Li, M D. Levine, X An and H. He, Saliency Detection Based on Frequency and Spatial Domain Analyses, BMVC 2011  
Attention via Information Maximization Code Saliency Detection http://www.cse.yorku.ca/~neil/AIM.zip N. Bruce and J. Tsotsos. Saliency based on information maximization. In NIPS, 2005  
Saliency detection: A spectral residual approach Code Saliency Detection http://www.klab.caltech.edu/~xhou/projects/spectralResidual/spectralresidual.html X. Hou and L. Zhang. Saliency detection: A spectral residual approach. CVPR, 2007  
Saliency detection using maximum symmetric surround Code Saliency Detection http://ivrg.epfl.ch/supplementary_material/RK_ICIP2010/index.html R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 2010  
Frequency-tuned salient region detection Code Saliency Detection http://ivrgwww.epfl.ch/supplementary_material/RK_CVPR09/index.html R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009  
Segmenting salient objects from images and videos Code Saliency Detection http://www.cse.oulu.fi/MVG/Downloads/saliency E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos. CVPR, 2010  
Diffusion Geometry Methods in Shape Analysis Tutorial Shape Analysis, Diffusion Geometry http://tosca.cs.technion.ac.il/book/course_eccv10.html A. Brontein and M. Bronstein, ECCV 2010 Tutorial  
Source Code Collection for Reproducible Research Link Source code http://www.csee.wvu.edu/~xinl/reproducible_research.html collected by Xin Li, Lane Dept of CSEE, West Virginia University  
Computer Vision Algorithm Implementations Link Source code http://www.cvpapers.com/rr.html CVPapers  
Robust Sparse Coding for Face Recognition Code Sparse Representation http://www4.comp.polyu.edu.hk/~cslzhang/code/RSC.zip M. Yang, L. Zhang, J. Yang and D. Zhang, “Robust Sparse Coding for Face Recognition,” CVPR 2011  
Sparse coding simulation software Code Sparse Representation http://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 Processing Code Sparse Representation http://www.cs.technion.ac.il/~elad/Various/Matlab-Package-Book.rar M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing  
Fisher Discrimination Dictionary Learning for Sparse Representation Code Sparse Representation http://www4.comp.polyu.edu.hk/~cslzhang/code/FDDL.zip M. Yang, L. Zhang, X. Feng and D. Zhang, Fisher Discrimination Dictionary Learning for Sparse Representation, ICCV 2011  
Efficient sparse coding algorithms Code Sparse Representation http://ai.stanford.edu/~hllee/softwares/nips06-sparsecoding.htm H. Lee, A. Battle, R. Rajat and A. Y. Ng, Efficient sparse coding algorithms, NIPS 2007  
A Linear Subspace Learning Approach via Sparse Coding Code Sparse Representation http://www4.comp.polyu.edu.hk/~cslzhang/code/LSL_SC.zip L. Zhang, P. Zhu, Q. Hu and D. Zhang, “A Linear Subspace Learning Approach via Sparse Coding,” ICCV 2011  
SPArse Modeling Software Code Sparse Representation http://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 Algorithms Talk Sparse Representation http://videolectures.net/nips09_bach_smm/ Francis R. Bach, INRIA  
Centralized Sparse Representation for Image Restoration Code Sparse Representation http://www4.comp.polyu.edu.hk/~cslzhang/code/CSR_IR.zip W. Dong, L. Zhang and G. Shi, “Centralized Sparse Representation for Image Restoration,” ICCV 2011  
A Tutorial on Spectral Clustering Tutorial Spectral Clustering http://web.mit.edu/~wingated/www/introductions/tutorial_on_spectral_clustering.pdf Ulrike von Luxburg, Max Planck Institute for Biological Cybernetics  
Statistical Learning Theory Talk Statistical Learning Theory http://videolectures.net/mlss04_taylor_slt/ John Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College London  
Stereo Evaluation Code Stereo http://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 Propagation Code Stereo http://www.cs.cityu.edu.hk/~qiyang/publications/code/cvpr-10-csbp/csbp.htm Q. Yang, L. Wang, and N. Ahuja, A Constant-Space Belief Propagation Algorithm for Stereo Matching, CVPR 2010  
libmv Code Structure from motion http://code.google.com/p/libmv/  
Structure from Motion toolbox for Matlab by Vincent Rabaud Code Structure from motion http://code.google.com/p/vincents-structure-from-motion-matlab-toolbox/  
FIT3D Code Structure from motion http://www.fit3d.info/  
VisualSFM : A Visual Structure from Motion System Code Structure from motion http://www.cs.washington.edu/homes/ccwu/vsfm/  
Structure and Motion Toolkit in Matlab Code Structure from motion http://cms.brookes.ac.uk/staff/PhilipTorr/Code/code_page_4.htm  
Nonrigid Structure from Motion Tutorial Structure from motion http://www.cs.cmu.edu/~yaser/ECCV2010Tutorial.html Y. Sheikh and Sohaib Khan, ECCV 2010 Tutorial  
Bundler Code Structure from motion http://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 Space Code Structure from motion http://cvlab.lums.edu.pk/nrsfm/index.html  
OpenSourcePhotogrammetry Code Structure from motion http://opensourcephotogrammetry.blogspot.com/  
Structured Prediction and Learning in Computer Vision Tutorial Structured Prediction http://www.nowozin.net/sebastian/cvpr2011tutorial/ S. Nowozin and C. Lampert, CVPR 2011 Tutorial  
Generalized Principal Component Analysis Code Subspace Learning http://www.vision.jhu.edu/downloads/main.php?dlID=c1 R. Vidal, Y. Ma and S. Sastry. Generalized Principal Component Analysis (GPCA), CVPR 2003  
Text recognition in the wild Code Text Recognition http://vision.ucsd.edu/~kai/grocr/ K. Wang, B. Babenko, and S. Belongie, End-to-end Scene Text Recognition, ICCV 2011  
Neocognitron for handwritten digit recognition Code Text Recognition http://visiome.neuroinf.jp/modules/xoonips/detail.php?item_id=375 K. Fukushima: "Neocognitron for handwritten digit recognition", Neurocomputing, 2003  
Image Quilting for Texture Synthesis and Transfer Code Texture Synthesis http://www.cs.cmu.edu/~efros/quilt_research_code.zip A. A. Efros and W. T. Freeman, Image Quilting for Texture Synthesis and Transfer, SIGGRAPH 2001  
Variational methods for computer vision Tutorial Variational Calculus http://cvpr.in.tum.de/tutorials/iccv2011 Daniel Cremers, Bastian Goldlucke, Thomas Pock, ICCV 2011 Tutorial  
Variational Methods in Computer Vision Tutorial Variational Calculus http://cvpr.cs.tum.edu/tutorials/eccv2010 D. Cremers, B. Goldlücke, T. Pock, ECCV 2010 Tutorial  
Understanding Visual Scenes Talk Visual Recognition http://videolectures.net/nips09_torralba_uvs/ Antonio Torralba, MIT  
Visual Recognition, University of Texas at Austin, Fall 2011 Course Visual Recognition http://www.cs.utexas.edu/~grauman/courses/fall2011/schedule.html Kristen Grauman  
Tracking using Pixel-Wise Posteriors Code Visual Tracking http://www.robots.ox.ac.uk/~cbibby/research_pwp.shtml C. Bibby and I. Reid, Tracking using Pixel-Wise Posteriors, ECCV 2008  
Visual Tracking with Histograms and Articulating Blocks Code Visual Tracking http://www.cise.ufl.edu/~smshahed/tracking.htm S. M. Shshed Nejhum, J.  Ho, and M.-H.Yang, Visual Tracking with Histograms and Articulating Blocks, CVPR 2008  
Lucas-Kanade affine template tracking Code Visual Tracking http://www.mathworks.com/matlabcentral/fileexchange/24677-lucas-kanade-affine-template-tracking S. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, IJCV 2002  
Visual Tracking Decomposition Code Visual Tracking http://cv.snu.ac.kr/research/~vtd/ J Kwon and K. M. Lee, Visual Tracking Decomposition, CVPR 2010  
GPU Implementation of Kanade-Lucas-Tomasi Feature Tracker Code Visual Tracking http://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 Sequences Code Visual Tracking http://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 Tracking Code Visual Tracking http://blogs.oregonstate.edu/hess/code/particles/  
Tracking with Online Multiple Instance Learning Code Visual Tracking http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml B. Babenko, M.-H. Yang, S. Belongie, Visual Tracking with Online Multiple Instance Learning, PAMI 2011  
KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker Code Visual Tracking http://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 Tracking Code Visual Tracking http://faculty.ucmerced.edu/mhyang/papers/iccv11a.html S. Wang, H. Lu, F. Yang, and M.-H. Yang, Superpixel Tracking, ICCV 2011  
L1 Tracking Code Visual Tracking http://www.dabi.temple.edu/~hbling/code_data.htm X. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009  
Online Discriminative Object Tracking with Local Sparse Representation Code Visual Tracking http://faculty.ucmerced.edu/mhyang/code/wacv12a_code.zip Q. Wang, F. Chen, W. Xu, and M.-H. Yang, Online Discriminative Object Tracking with Local Sparse Representation, WACV 2012  
Incremental Learning for Robust Visual Tracking Code Visual Tracking http://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 trackers Code Visual Tracking http://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 Objects Code Visual Tracking http://www.ics.uci.edu/~hpirsiav/papers/tracking_cvpr11_release_v1.0.tar.gz H. Pirsiavash, D. Ramanan, C. Fowlkes. "Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects, CVPR 2011  
Object Tracking Code Visual Tracking http://plaza.ufl.edu/lvtaoran/object%20tracking.htm