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 2010GIST 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. 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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, 2011Sprarse 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. 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