图像处理与计算机视觉资源汇总——论文+代码+教材+视频等等

历时一个多月，终于用业余时间把这些资料整理出来了，总算了却了一块心病，也不至于再看着一堆资料发愁了。以后可能会有些小修小补，但不会有太大的变化了。万里长征走完了第一步，剩下的就是理解和消化了。借新浪ishare共享出来，希望能够对你的科研也有一定的帮助。

UIUC的Jia-Bin Huang同学整理很多计算机视觉的资源，主要是代码，在这里也mark一下。

https://netfiles.uiuc.edu/jbhuang1/www/

最后简单统计一下各个年份出现的频率

文章总数：372

2012年： 10

2011年： 20

2010年： 20

2009年： 14

2008年： 18

2007年： 13

2006年： 14

2005年： 9

2004年： 24

2003年： 22

2002年： 21

2001年： 21

2000年： 23

1999年： 10

1998年： 22

1997年： 8

1996年： 9

1995年： 9

1994年： 7

1993年： 5

1992年： 11

1991年： 5

1990年： 6

1980-1989： 22

1960-1979： 9

Jia-Bin的Computer Vision Resource的内容（纯copy）

323个Item Type Topic Name Reference Link Code Structure from motion libmv   http://code.google.com/p/libmv/ Code Dimension Reduction LLE   http://www.cs.nyu.edu/~roweis/lle/code.html Code Clustering Spectral Clustering - UCSD Project   http://vision.ucsd.edu/~sagarwal/spectral-0.2.tgz Code Clustering K-Means - Oxford Code   http://www.cs.ucf.edu/~vision/Code/vggkmeans.zip Code Image Deblurring Non-blind deblurring (and blind denoising) with integrated noise estimation U. Schmidt, K. Schelten, and S. Roth. Bayesian deblurring with integrated noise estimation, CVPR 2011 http://www.gris.tu-darmstadt.de/research/visinf/software/index.en.htm Code Structure from motion Structure from Motion toolbox for Matlab by Vincent Rabaud   http://code.google.com/p/vincents-structure-from-motion-matlab-toolbox/ Code Multiple View Geometry Matlab Functions for Multiple View Geometry   http://www.robots.ox.ac.uk/~vgg/hzbook/code/ Code Object Detection Max-Margin Hough Transform S. Maji and J. Malik, Object Detection Using a Max-Margin Hough Transform. CVPR 2009 http://www.cs.berkeley.edu/~smaji/projects/max-margin-hough/ Code Image Segmentation SLIC Superpixels R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010 http://ivrg.epfl.ch/supplementary_material/RK_SLICSuperpixels/index.html Code Visual Tracking Tracking using Pixel-Wise Posteriors C. Bibby and I. Reid, Tracking using Pixel-Wise Posteriors, ECCV 2008 http://www.robots.ox.ac.uk/~cbibby/research_pwp.shtml Code Visual Tracking Visual Tracking with Histograms and Articulating Blocks S. M. Shshed Nejhum, J. Ho, and M.-H.Yang, Visual Tracking with Histograms and Articulating Blocks, CVPR 2008 http://www.cise.ufl.edu/~smshahed/tracking.htm Code Sparse Representation Robust Sparse Coding for Face Recognition M. Yang, L. Zhang, J. Yang and D. Zhang, “Robust Sparse Coding for Face Recognition,” CVPR 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/RSC.zip Code Feature Detection andFeature Extraction Groups of Adjacent Contour Segments V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, Groups of Adjacent Contour Segments for Object Detection, PAMI, 2007 http://www.robots.ox.ac.uk/~vgg/share/ferrari/release-kas-v102.tgz Code Density Estimation Kernel Density Estimation Toolbox   http://www.ics.uci.edu/~ihler/code/kde.html Code Illumination, Reflectance, and Shadow Ground shadow detection J.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs, ECCV 2010 http://www.jflalonde.org/software.html#shadowDetection Code Image Denoising,Image Super-resolution, andImage Deblurring Learning Models of Natural Image Patches D. Zoran and Y. Weiss, From Learning Models of Natural Image Patches to Whole Image Restoration, ICCV, 2011 http://www.cs.huji.ac.il/~daniez/ Code Illumination, Reflectance, and Shadow Estimating Natural Illumination from a Single Outdoor Image J-F. Lalonde, A. A. Efros, S. G. Narasimhan, Estimating Natural Illumination from a Single Outdoor Image , ICCV 2009 http://www.cs.cmu.edu/~jlalonde/software.html#skyModel Code Visual Tracking Lucas-Kanade affine template tracking S. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, IJCV 2002 http://www.mathworks.com/matlabcentral/fileexchange/24677-lucas-kanade-affine-template-tracking Code Saliency Detection Saliency-based video segmentation K. Fukuchi, K. Miyazato, A. Kimura, S. Takagi and J. Yamato, Saliency-based video segmentation with graph cuts and sequentially updated priors, ICME 2009 http://www.brl.ntt.co.jp/people/akisato/saliency3.html Code Dimension Reduction Laplacian Eigenmaps   http://www.cse.ohio-state.edu/~mbelkin/algorithms/Laplacian.tar Code Illumination, Reflectance, and Shadow What Does the Sky Tell Us About the Camera? J-F. Lalonde, S. G. Narasimhan, A. A. Efros, What Does the Sky Tell Us About the Camera?, ECCV 2008 http://www.cs.cmu.edu/~jlalonde/software.html#skyModel Code Image Filtering SVM for Edge-Preserving Filtering Q. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering, CVPR 2010 http://vision.ai.uiuc.edu/~qyang6/publications/code/cvpr-10-svmbf/program_video_conferencing.zip Code Image Segmentation Recovering Occlusion Boundaries from a Single Image D. Hoiem, A. Stein, A. A. Efros, M. Hebert, Recovering Occlusion Boundaries from a Single Image, ICCV 2007. http://www.cs.cmu.edu/~dhoiem/software/ Code Visual Tracking Visual Tracking Decomposition J Kwon and K. M. Lee, Visual Tracking Decomposition, CVPR 2010 http://cv.snu.ac.kr/research/~vtd/ Code Visual Tracking GPU Implementation of Kanade-Lucas-Tomasi Feature Tracker S. N Sinha, J.-M. Frahm, M. Pollefeys and Y. Genc, Feature Tracking and Matching in Video Using Programmable Graphics Hardware, MVA, 2007 http://cs.unc.edu/~ssinha/Research/GPU_KLT/ Code Object Detection Recognition using regions C. Gu, J. J. Lim, P. Arbelaez, and J. Malik, CVPR 2009 http://www.cs.berkeley.edu/~chunhui/publications/cvpr09_v2.zip Code Saliency Detection Saliency Using Natural statistics L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 2008 http://cseweb.ucsd.edu/~l6zhang/ Code Image Filtering Local Laplacian Filters S. Paris, S. Hasinoff, J. Kautz, Local Laplacian Filters: Edge-Aware Image Processing with a Laplacian Pyramid, SIGGRAPH 2011 http://people.csail.mit.edu/sparis/publi/2011/siggraph/matlab_source_code.zip Code Common Visual Pattern Discovery Sketching the Common S. Bagon, O. Brostovsky, M. Galun and M. Irani, Detecting and Sketching the Common, CVPR 2010 http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/SketchCommonCVPR10_v1.1.tar.gz Code Image Denoising BLS-GSM   http://decsai.ugr.es/~javier/denoise/ Code Camera Calibration Epipolar Geometry Toolbox G.L. Mariottini, D. Prattichizzo, EGT: a Toolbox for Multiple View Geometry and Visual Servoing, IEEE Robotics & Automation Magazine, 2005 http://egt.dii.unisi.it/ Code Depth Sensor Kinect SDK http://www.microsoft.com/en-us/kinectforwindows/ http://www.microsoft.com/en-us/kinectforwindows/ Code Image Super-resolution Self-Similarities for Single Frame Super-Resolution C.-Y. Yang, J.-B. Huang, and M.-H. Yang, Exploiting Self-Similarities for Single Frame Super-Resolution, ACCV 2010 https://eng.ucmerced.edu/people/cyang35/ACCV10.zip Code Image Denoising Gaussian Field of Experts   http://www.cs.huji.ac.il/~yweiss/BRFOE.zip Code Object Detection Poselet L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009 http://www.eecs.berkeley.edu/~lbourdev/poselets/ Code Kernels and Distances Efficient Earth Mover's Distance with L1 Ground Distance (EMD_L1) H. Ling and K. Okada, An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison, PAMI 2007 http://www.dabi.temple.edu/~hbling/code/EmdL1_v3.zip Code Nearest Neighbors Matching Spectral Hashing Y. Weiss, A. Torralba, R. Fergus, Spectral Hashing, NIPS 2008 http://www.cs.huji.ac.il/~yweiss/SpectralHashing/ Code Image Denoising Field of Experts   http://www.cs.brown.edu/~roth/research/software.html Code Image Segmentation Multiscale Segmentation Tree E. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,” ACCV 2009 and N. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996 http://vision.ai.uiuc.edu/segmentation Code Multiple Instance Learning MILIS Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010   Code Nearest Neighbors Matching FLANN: Fast Library for Approximate Nearest Neighbors   http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN Code Feature Detection andFeature Extraction Maximally stable extremal regions (MSER) - VLFeat J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002 http://www.vlfeat.org/ Code Alpha Matting Spectral Matting A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008 http://www.vision.huji.ac.il/SpectralMatting/ Code Multi-View Stereo Patch-based Multi-view Stereo Software Y. Furukawa and J. Ponce, Accurate, Dense, and Robust Multi-View Stereopsis, PAMI 2009 http://grail.cs.washington.edu/software/pmvs/ Code Clustering Self-Tuning Spectral Clustering   http://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.html Code Feature Extraction andObject Detection Histogram of Oriented Graidents - OLT for windows N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005 http://www.computing.edu.au/~12482661/hog.html Code Image Understanding Nonparametric Scene Parsing via Label Transfer C. Liu, J. Yuen, and Antonio Torralba, Nonparametric Scene Parsing via Label Transfer, PAMI 2011 http://people.csail.mit.edu/celiu/LabelTransfer/index.html Code Multiple Kernel Learning DOGMA F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010 http://dogma.sourceforge.net/ Code Distance Metric Learning Matlab Toolkit for Distance Metric Learning   http://www.cs.cmu.edu/~liuy/distlearn.htm Code Optical Flow Black and Anandan's Optical Flow   http://www.cs.brown.edu/~dqsun/code/ba.zip Code Text Recognition Text recognition in the wild K. Wang, B. Babenko, and S. Belongie, End-to-end Scene Text Recognition, ICCV 2011 http://vision.ucsd.edu/~kai/grocr/ Code MRF Optimization MRF Minimization Evaluation R. Szeliski et al., A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, PAMI, 2008 http://vision.middlebury.edu/MRF/ Code Saliency Detection Context-aware saliency detection S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, 2010. http://webee.technion.ac.il/labs/cgm/Computer-Graphics-Multimedia/Software/Saliency/Saliency.html Code Saliency Detection Learning to Predict Where Humans Look T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look, ICCV, 2009 http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html Code Stereo Stereo Evaluation D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV 2001 http://vision.middlebury.edu/stereo/ Code Image Segmentation Quick-Shift A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking, ECCV, 2008 http://www.vlfeat.org/overview/quickshift.html Code Saliency Detection Graph-based visual saliency J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007 http://www.klab.caltech.edu/~harel/share/gbvs.php Code Clustering K-Means - VLFeat   http://www.vlfeat.org/ Code Object Detection A simple object detector with boosting ICCV 2005 short courses on Recognizing and Learning Object Categories http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html Code Image Quality Assessment Structural SIMilarity   https://ece.uwaterloo.ca/~z70wang/research/ssim/ Code Structure from motion FIT3D   http://www.fit3d.info/ Code Image Denoising BM3D   http://www.cs.tut.fi/~foi/GCF-BM3D/ Code Saliency Detection Discriminant Saliency for Visual Recognition from Cluttered Scenes D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes, NIPS, 2004 http://www.svcl.ucsd.edu/projects/saliency/ Code Image Denoising Nonlocal means with cluster trees T. Brox, O. Kleinschmidt, D. Cremers, Efficient nonlocal means for denoising of textural patterns, TIP 2008 http://lmb.informatik.uni-freiburg.de/resources/binaries/nlmeans_brox_tip08Linux64.zip Code Saliency Detection Global Contrast based Salient Region Detection M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection. CVPR, 2011 http://cg.cs.tsinghua.edu.cn/people/~cmm/saliency/ Code Visual Tracking Motion Tracking in Image Sequences C. Stauffer and W. E. L. Grimson. Learning patterns of activity using real-time tracking, PAMI, 2000 http://www.cs.berkeley.edu/~flw/tracker/ Code Saliency Detection Itti, Koch, and Niebur' saliency detection L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 1998 http://www.saliencytoolbox.net/ Code Feature Detection,Feature Extraction, andAction Recognition Space-Time Interest Points (STIP) I. Laptev, On Space-Time Interest Points, IJCV, 2005 and I. Laptev and T. Lindeberg, On Space-Time Interest Points, IJCV 2005 http://www.irisa.fr/vista/Equipe/People/Laptev/download/stip-1.1-winlinux.zip andhttp://www.nada.kth.se/cvap/abstracts/cvap284.html Code Texture Synthesis Image Quilting for Texture Synthesis and Transfer A. A. Efros and W. T. Freeman, Image Quilting for Texture Synthesis and Transfer, SIGGRAPH 2001 http://www.cs.cmu.edu/~efros/quilt_research_code.zip Code Image Denoising Non-local Means   http://dmi.uib.es/~abuades/codis/NLmeansfilter.m Code Low-Rank Modeling TILT: Transform Invariant Low-rank Textures Z. Zhang, A. Ganesh, X. Liang, and Y. Ma, TILT: Transform Invariant Low-rank Textures, IJCV 2011 http://perception.csl.uiuc.edu/matrix-rank/tilt.html Code Object Proposal Objectness measure B. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010 http://www.vision.ee.ethz.ch/~calvin/objectness/objectness-release-v1.01.tar.gz Code Image Filtering Real-time O(1) Bilateral Filtering Q. Yang, K.-H. Tan and N. Ahuja, Real-time O(1) Bilateral Filtering, CVPR 2009 http://vision.ai.uiuc.edu/~qyang6/publications/code/qx_constant_time_bilateral_filter_ss.zip Code Image Quality Assessment SPIQA   http://vision.ai.uiuc.edu/~bghanem2/shared_code/SPIQA_code.zip Code Object Recognition Biologically motivated object recognition T. Serre, L. Wolf and T. Poggio. Object recognition with features inspired by visual cortex, CVPR 2005 http://cbcl.mit.edu/software-datasets/standardmodel/index.html Code Illumination, Reflectance, and Shadow Shadow Detection using Paired Region R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011 http://www.cs.illinois.edu/homes/guo29/projects/shadow.html Code Illumination, Reflectance, and Shadow Real-time Specular Highlight Removal Q. Yang, S. Wang and N. Ahuja, Real-time Specular Highlight Removal Using Bilateral Filtering, ECCV 2010 http://www.cs.cityu.edu.hk/~qiyang/publications/code/eccv-10.zip Code MRF Optimization Max-flow/min-cut Y. Boykov and V. Kolmogorov, An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, PAMI 2004 http://vision.csd.uwo.ca/code/maxflow-v3.01.zip Code Optical Flow Optical Flow Evaluation S. Baker et al. A Database and Evaluation Methodology for Optical Flow, IJCV, 2011 http://vision.middlebury.edu/flow/ Code Image Super-resolution MRF for image super-resolution 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 http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.html Code MRF Optimization Planar Graph Cut F. R. Schmidt, E. Toppe and D. Cremers, Efficient Planar Graph Cuts with Applications in Computer Vision, CVPR 2009 http://vision.csd.uwo.ca/code/PlanarCut-v1.0.zip Code Object Detection Feature Combination P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009 http://www.vision.ee.ethz.ch/~pgehler/projects/iccv09/index.html Code Structure from motion VisualSFM : A Visual Structure from Motion System   http://www.cs.washington.edu/homes/ccwu/vsfm/ Code Nearest Neighbors Matching ANN: Approximate Nearest Neighbor Searching   http://www.cs.umd.edu/~mount/ANN/ Code Saliency Detection Learning Hierarchical Image Representation with Sparsity, Saliency and Locality J. Yang and M.-H. Yang, Learning Hierarchical Image Representation with Sparsity, Saliency and Locality, BMVC 2011   Code Optical Flow Optical Flow by Deqing Sun D. Sun, S. Roth, M. J. Black, Secrets of Optical Flow Estimation and Their Principles, CVPR, 2010 http://www.cs.brown.edu/~dqsun/code/flow_code.zip Code Image Understanding Discriminative Models for Multi-Class Object Layout C. Desai, D. Ramanan, C. Fowlkes. "Discriminative Models for Multi-Class Object Layout, IJCV 2011 http://www.ics.uci.edu/~desaic/multiobject_context.zip Code Graph Matching Hyper-graph Matching via Reweighted Random Walks J. Lee, M. Cho, K. M. Lee. "Hyper-graph Matching via Reweighted Random Walks", CVPR 2011 http://cv.snu.ac.kr/research/~RRWHM/ Code Object Detection Hough Forests for Object Detection J. Gall and V. Lempitsky, Class-Specific Hough Forests for Object Detection, CVPR, 2009 http://www.vision.ee.ethz.ch/~gallju/projects/houghforest/index.html Code Object Discovery Using Multiple Segmentations to Discover Objects and their Extent in Image Collections 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 http://people.csail.mit.edu/brussell/research/proj/mult_seg_discovery/index.html Code Dimension Reduction Diffusion maps   http://www.stat.cmu.edu/~annlee/software.htm Code Multiple Kernel Learning SHOGUN S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning. JMLR, 2006 http://www.shogun-toolbox.org/ Code Distance Transformation Distance Transforms of Sampled Functions   http://people.cs.uchicago.edu/~pff/dt/ Code Image Filtering Image smoothing via L0 Gradient Minimization L. Xu, C. Lu, Y. Xu, J. Jia, Image smoothing via L0 Gradient Minimization, SIGGRAPH Asia 2011 http://www.cse.cuhk.edu.hk/~leojia/projects/L0smoothing/L0smoothing.zip Code Feature Extraction PCA-SIFT Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004 http://www.cs.cmu.edu/~yke/pcasift/ Code Visual Tracking Particle Filter Object Tracking   http://blogs.oregonstate.edu/hess/code/particles/ Code Feature Extraction sRD-SIFT M. Lourenco, J. P. Barreto and A. Malti, Feature Detection and Matching in Images with Radial Distortion, ICRA 2010 http://arthronav.isr.uc.pt/~mlourenco/srdsift/index.html# Code Multiple Instance Learning MILES Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection. PAMI 2006 http://infolab.stanford.edu/~wangz/project/imsearch/SVM/PAMI06/ Code Action Recognition Dense Trajectories Video Description H. Wang and A. Klaser and C. Schmid and C.- L. Liu, Action Recognition by Dense Trajectories, CVPR, 2011 http://lear.inrialpes.fr/people/wang/dense_trajectories Code Image Segmentation Efficient Graph-based Image Segmentation - C++ code P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004 http://people.cs.uchicago.edu/~pff/segment/ Code Object Proposal Parametric min-cut J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010 http://sminchisescu.ins.uni-bonn.de/code/cpmc/ Code Common Visual Pattern Discovery Common Visual Pattern Discovery via Spatially Coherent Correspondences H. Liu, S. Yan, "Common Visual Pattern Discovery via Spatially Coherent Correspondences", CVPR 2010 https://sites.google.com/site/lhrbss/home/papers/SimplifiedCode.zip?attredirects=0 Code Sparse Representation Sparse coding simulation software Olshausen BA, Field DJ, "Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images", Nature 1996 http://redwood.berkeley.edu/bruno/sparsenet/ Code MRF Optimization Max-flow/min-cut for massive grids A. Delong and Y. Boykov, A Scalable Graph-Cut Algorithm for N-D Grids, CVPR 2008 http://vision.csd.uwo.ca/code/regionpushrelabel-v1.03.zip Code Optical Flow Horn and Schunck's Optical Flow   http://www.cs.brown.edu/~dqsun/code/hs.zip Code Sparse Representation Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing http://www.cs.technion.ac.il/~elad/Various/Matlab-Package-Book.rar Code Image Understanding Towards Total Scene Understanding L.-J. Li, R. Socher and Li F.-F.. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework, CVPR 2009 http://vision.stanford.edu/projects/totalscene/index.html Code Camera Calibration Camera Calibration Toolbox for Matlab http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/ref.html http://www.vision.caltech.edu/bouguetj/calib_doc/ Code Image Segmentation Turbepixels A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric Flows, PAMI 2009 http://www.cs.toronto.edu/~babalex/research.html Code Feature Detection Edge Foci Interest Points L. Zitnickand K. Ramnath, Edge Foci Interest Points, ICCV, 2011 http://research.microsoft.com/en-us/um/people/larryz/edgefoci/edge_foci.htm Code Feature Extraction Local Self-Similarity Descriptor E. Shechtman and M. Irani. Matching local self-similarities across images and videos, CVPR, 2007 http://www.robots.ox.ac.uk/~vgg/software/SelfSimilarity/ Code Subspace Learning Generalized Principal Component Analysis R. Vidal, Y. Ma and S. Sastry. Generalized Principal Component Analysis (GPCA), CVPR 2003 http://www.vision.jhu.edu/downloads/main.php?dlID=c1 Code Camera Calibration EasyCamCalib J. Barreto, J. Roquette, P. Sturm, and F. Fonseca, Automatic camera calibration applied to medical endoscopy, BMVC, 2009 http://arthronav.isr.uc.pt/easycamcalib/ Code Image Segmentation Superpixel by Gerg Mori X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003 http://www.cs.sfu.ca/~mori/research/superpixels/ Code Image Understanding Object Bank 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 http://vision.stanford.edu/projects/objectbank/index.html Code Saliency Detection Spectrum Scale Space based Visual Saliency J Li, M D. Levine, X An and H. He, Saliency Detection Based on Frequency and Spatial Domain Analyses, BMVC 2011 http://www.cim.mcgill.ca/~lijian/saliency.htm Code Sparse Representation Fisher Discrimination Dictionary Learning for Sparse Representation M. Yang, L. Zhang, X. Feng and D. Zhang, Fisher Discrimination Dictionary Learning for Sparse Representation, ICCV 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/FDDL.zip Code Object Detection Cascade Object Detection with Deformable Part Models P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models. CVPR, 2010 http://people.cs.uchicago.edu/~rbg/star-cascade/ Code Object Segmentation Sparse to Dense Labeling P. Ochs, T. Brox, Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions, ICCV 2011 http://lmb.informatik.uni-freiburg.de/resources/binaries/SparseToDenseLabeling.tar.gz Code Optical Flow Dense Point Tracking N. Sundaram, T. Brox, K. KeutzerDense point trajectories by GPU-accelerated large displacement optical flow, ECCV 2010 http://lmb.informatik.uni-freiburg.de/resources/binaries/ Code Visual Tracking Tracking with Online Multiple Instance Learning B. Babenko, M.-H. Yang, S. Belongie, Visual Tracking with Online Multiple Instance Learning, PAMI 2011 http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml Code Graph Matching Reweighted Random Walks for Graph Matching M. Cho, J. Lee, and K. M. Lee, Reweighted Random Walks for Graph Matching, ECCV 2010 http://cv.snu.ac.kr/research/~RRWM/ Code Machine Learning Statistical Pattern Recognition Toolbox M.I. Schlesinger, V. Hlavac: Ten lectures on the statistical and structural pattern recognition, Kluwer Academic Publishers, 2002 http://cmp.felk.cvut.cz/cmp/software/stprtool/ Code Image Super-resolution Sprarse coding super-resolution J. Yang, J. Wright, T. S. Huang, and Y. Ma. Image super-resolution via sparse representation, TIP 2010 http://www.ifp.illinois.edu/~jyang29/ScSR.htm Code Object Detection Discriminatively Trained Deformable Part Models P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan.Object Detection with Discriminatively Trained Part Based Models, PAMI, 2010 http://people.cs.uchicago.edu/~pff/latent/ Code Multiple Instance Learning MIForests C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010 http://www.ymer.org/amir/software/milforests/ Code Optical Flow Large Displacement Optical Flow T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, PAMI 2011 http://lmb.informatik.uni-freiburg.de/resources/binaries/ Code Multiple View Geometry MATLAB and Octave Functions for Computer Vision and Image Processing P. D. Kovesi. MATLAB and Octave Functions for Computer Vision and Image Processing, http://www.csse.uwa.edu.au/~pk/research/matlabfns http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.html Code Image Filtering Anisotropic Diffusion P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, PAMI 1990 http://www.mathworks.com/matlabcentral/fileexchange/14995-anisotropic-diffusion-perona-malik Code Feature Detection andFeature Extraction Geometric Blur A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR, 2005 http://www.robots.ox.ac.uk/~vgg/software/MKL/ Code Low-Rank Modeling Low-Rank Matrix Recovery and Completion   http://perception.csl.uiuc.edu/matrix-rank/sample_code.html Code Object Detection A simple parts and structure object detector ICCV 2005 short courses on Recognizing and Learning Object Categories http://people.csail.mit.edu/fergus/iccv2005/partsstructure.html Code Kernels and Distances Diffusion-based distance H. Ling and K. Okada, Diffusion Distance for Histogram Comparison, CVPR 2006 http://www.dabi.temple.edu/~hbling/code/DD_v1.zip Code Image Denoising K-SVD   http://www.cs.technion.ac.il/~ronrubin/Software/ksvdbox13.zip Code Multiple Kernel Learning SimpleMKL A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl. JMRL, 2008 http://asi.insa-rouen.fr/enseignants/~arakotom/code/mklindex.html Code Feature Extraction Pyramids of Histograms of Oriented Gradients (PHOG) A. Bosch, A. Zisserman, and X. Munoz, Representing shape with a spatial pyramid kernel, CIVR, 2007 http://www.robots.ox.ac.uk/~vgg/research/caltech/phog/phog.zip Code Sparse Representation Efficient sparse coding algorithms H. Lee, A. Battle, R. Rajat and A. Y. Ng, Efficient sparse coding algorithms, NIPS 2007 http://ai.stanford.edu/~hllee/softwares/nips06-sparsecoding.htm Code Multi-View Stereo Clustering Views for Multi-view Stereo Y. Furukawa, B. Curless, S. M. Seitz, and R. Szeliski, Towards Internet-scale Multi-view Stereo, CVPR 2010 http://grail.cs.washington.edu/software/cmvs/ Code Multi-View Stereo Multi-View Stereo Evaluation S. Seitz et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006 http://vision.middlebury.edu/mview/ Code Structure from motion Structure and Motion Toolkit in Matlab   http://cms.brookes.ac.uk/staff/PhilipTorr/Code/code_page_4.htm Code Pose Estimation Training Deformable Models for Localization Ramanan, D. "Learning to Parse Images of Articulated Bodies." NIPS 2006 http://www.ics.uci.edu/~dramanan/papers/parse/index.html Code Low-Rank Modeling RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition 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 http://perception.csl.uiuc.edu/matrix-rank/rasl.html Code Dimension Reduction ISOMAP   http://isomap.stanford.edu/ Code Alpha Matting Learning-based Matting Y. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009 http://www.mathworks.com/matlabcentral/fileexchange/31412 Code Image Segmentation Normalized Cut J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000 http://www.cis.upenn.edu/~jshi/software/ Code Image Denoising andStereo Matching Efficient Belief Propagation for Early Vision P. F. Felzenszwalb and D. P. Huttenlocher, Efficient Belief Propagation for Early Vision, IJCV, 2006 http://www.cs.brown.edu/~pff/bp/ Code Sparse Representation A Linear Subspace Learning Approach via Sparse Coding L. Zhang, P. Zhu, Q. Hu and D. Zhang, “A Linear Subspace Learning Approach via Sparse Coding,” ICCV 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/LSL_SC.zip Code Text Recognition Neocognitron for handwritten digit recognition K. Fukushima: "Neocognitron for handwritten digit recognition", Neurocomputing, 2003 http://visiome.neuroinf.jp/modules/xoonips/detail.php?item_id=375 Code Image Classification Sparse Coding for Image Classification J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification, CVPR, 2009 http://www.ifp.illinois.edu/~jyang29/ScSPM.htm Code Nearest Neighbors Matching LDAHash: Binary Descriptors for Matching in Large Image Databases C. Strecha, A. M. Bronstein, M. M. Bronstein and P. Fua. LDAHash: Improved matching with smaller descriptors, PAMI, 2011. http://cvlab.epfl.ch/research/detect/ldahash/index.php Code Object Segmentation ClassCut for Unsupervised Class Segmentation B. Alexe, T. Deselaers and V. Ferrari, ClassCut for Unsupervised Class Segmentation, ECCV 2010 http://www.vision.ee.ethz.ch/~calvin/classcut/ClassCut-release.zip Code Image Quality Assessment Feature SIMilarity Index   http://www4.comp.polyu.edu.hk/~cslzhang/IQA/FSIM/FSIM.htm Code Saliency Detection Attention via Information Maximization N. Bruce and J. Tsotsos. Saliency based on information maximization. In NIPS, 2005 http://www.cse.yorku.ca/~neil/AIM.zip Code Image Denoising What makes a good model of natural images ? Y. Weiss and W. T. Freeman, CVPR 2007 http://www.cs.huji.ac.il/~yweiss/BRFOE.zip Code Image Segmentation Mean-Shift Image Segmentation - Matlab Wrapper D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002 http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/edison_matlab_interface.tar.gz Code Object Segmentation Geodesic Star Convexity for Interactive Image Segmentation V. Gulshan, C. Rother, A. Criminisi, A. Blake and A. Zisserman. Geodesic star convexity for interactive image segmentation http://www.robots.ox.ac.uk/~vgg/software/iseg/index.shtml Code Feature Detection andFeature Extraction Affine-SIFT J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009 http://www.ipol.im/pub/algo/my_affine_sift/ Code MRF Optimization Multi-label optimization Y. Boykov, O. Verksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001 http://vision.csd.uwo.ca/code/gco-v3.0.zip Code Feature Detection andFeature Extraction Scale-invariant feature transform (SIFT) - Demo Software D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. http://www.cs.ubc.ca/~lowe/keypoints/ Code Visual Tracking KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker B. D. Lucas and T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. IJCAI, 1981 http://www.ces.clemson.edu/~stb/klt/ Code Feature Detection andFeature Extraction Affine Covariant Features T. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors: A Survey, Foundations and Trends in Computer Graphics and Vision, 2008 http://www.robots.ox.ac.uk/~vgg/research/affine/ Code Image Segmentation Segmenting Scenes by Matching Image Composites B. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, NIPS 2009 http://www.cs.washington.edu/homes/bcr/projects/SceneComposites/index.html Code Image Segmentation OWT-UCM Hierarchical Segmentation P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011 http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html Code Feature Matching andImage Classification The Pyramid Match: Efficient Matching for Retrieval and Recognition K. Grauman and T. Darrell. The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005 http://www.cs.utexas.edu/~grauman/research/projects/pmk/pmk_projectpage.htm Code Alpha Matting Bayesian Matting Y. Y. Chuang, B. Curless, D. H. Salesin, and R. Szeliski, A Bayesian Approach to Digital Matting, CVPR, 2001 http://www1.idc.ac.il/toky/CompPhoto-09/Projects/Stud_projects/Miki/index.html Code Image Deblurring Richardson-Lucy Deblurring for Scenes under Projective Motion Path Y.-W. Tai, P. Tan, M. S. Brown: Richardson-Lucy Deblurring for Scenes under Projective Motion Path, PAMI 2011 http://yuwing.kaist.ac.kr/projects/projectivedeblur/projectivedeblur_files/ProjectiveDeblur.zip Code Pose Estimation Articulated Pose Estimation using Flexible Mixtures of Parts Y. Yang, D. Ramanan, Articulated Pose Estimation using Flexible Mixtures of Parts, CVPR 2011 http://phoenix.ics.uci.edu/software/pose/ Code Feature Extraction BRIEF: Binary Robust Independent Elementary Features M. Calonder, V. Lepetit, C. Strecha, P. Fua, BRIEF: Binary Robust Independent Elementary Features, ECCV 2010 http://cvlab.epfl.ch/research/detect/brief/ Code Feature Extraction Global and Efficient Self-Similarity T. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection. CVPR 2010 and T. Deselaers, V. Ferrari, Global and Efficient Self-Similarity for Object Classification and Detection, CVPR 2010 http://www.vision.ee.ethz.ch/~calvin/gss/selfsim_release1.0.tgz Code Image Super-resolution Multi-frame image super-resolution Pickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis http://www.robots.ox.ac.uk/~vgg/software/SR/index.html Code Feature Detection andFeature Extraction Scale-invariant feature transform (SIFT) - Library D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. http://blogs.oregonstate.edu/hess/code/sift/ Code Image Denoising Clustering-based Denoising P. Chatterjee and P. Milanfar, Clustering-based Denoising with Locally Learned Dictionaries (K-LLD), TIP, 2009 http://users.soe.ucsc.edu/~priyam/K-LLD/ Code Object Recognition Recognition by Association via Learning Per-exemplar Distances T. Malisiewicz, A. A. Efros, Recognition by Association via Learning Per-exemplar Distances, CVPR 2008 http://www.cs.cmu.edu/~tmalisie/projects/cvpr08/dfuns.tar.gz Code Visual Tracking Superpixel Tracking S. Wang, H. Lu, F. Yang, and M.-H. Yang, Superpixel Tracking, ICCV 2011 http://faculty.ucmerced.edu/mhyang/papers/iccv11a.html Code Sparse Representation SPArse Modeling Software J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online Learning for Matrix Factorization and Sparse Coding, JMLR 2010 http://www.di.ens.fr/willow/SPAMS/ Code Saliency Detection Saliency detection: A spectral residual approach X. Hou and L. Zhang. Saliency detection: A spectral residual approach. CVPR, 2007 http://www.klab.caltech.edu/~xhou/projects/spectralResidual/spectralresidual.html Code Image Filtering Guided Image Filtering K. He, J. Sun, X. Tang, Guided Image Filtering, ECCV 2010 http://personal.ie.cuhk.edu.hk/~hkm007/eccv10/guided-filter-code-v1.rar Code Kernels and Distances Fast Directional Chamfer Matching   http://www.umiacs.umd.edu/~mingyliu/src/fdcm_matlab_wrapper_v0.2.zip Code Visual Tracking L1 Tracking X. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009 http://www.dabi.temple.edu/~hbling/code_data.htm Code Object Proposal Region-based Object Proposal I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010 http://vision.cs.uiuc.edu/proposals/ Code Object Detection Ensemble of Exemplar-SVMs for Object Detection and Beyond T. Malisiewicz, A. Gupta, A. A. Efros, Ensemble of Exemplar-SVMs for Object Detection and Beyond , ICCV 2011 http://www.cs.cmu.edu/~tmalisie/projects/iccv11/ Code Dimension Reduction Dimensionality Reduction Toolbox   http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html Code Object Detection Viola-Jones Object Detection P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR, 2001 http://pr.willowgarage.com/wiki/FaceDetection Code Object Detection Implicit Shape Model B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation, IJCV, 2008 http://www.vision.ee.ethz.ch/~bleibe/code/ism.html Code Saliency Detection Saliency detection using maximum symmetric surround R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 2010 http://ivrg.epfl.ch/supplementary_material/RK_ICIP2010/index.html Code Image Filtering Fast Bilateral Filter S. Paris and F. Durand, A Fast Approximation of the Bilateral Filter using a Signal Processing Approach, ECCV, 2006 http://people.csail.mit.edu/sparis/bf/ Code Machine Learning FastICA package for MATLAB http://research.ics.tkk.fi/ica/book/ http://research.ics.tkk.fi/ica/fastica/ Code Feature Detection andFeature Extraction Maximally stable extremal regions (MSER) J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002 http://www.robots.ox.ac.uk/~vgg/research/affine/ Code Structure from motion Bundler N. Snavely, S M. Seitz, R Szeliski. Photo Tourism: Exploring image collections in 3D. SIGGRAPH 2006 http://phototour.cs.washington.edu/bundler/ Code Visual Tracking Online Discriminative Object Tracking with Local Sparse Representation Q. Wang, F. Chen, W. Xu, and M.-H. Yang, Online Discriminative Object Tracking with Local Sparse Representation, WACV 2012 http://faculty.ucmerced.edu/mhyang/code/wacv12a_code.zip Code Alpha Matting Closed Form Matting A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting, PAMI 2008. http://people.csail.mit.edu/alevin/matting.tar.gz Code Image Filtering 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 http://grail.cs.washington.edu/projects/gradientshop/ Code Visual Tracking Incremental Learning for Robust Visual Tracking D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007 http://www.cs.toronto.edu/~dross/ivt/ Code Feature Detection andFeature Extraction Color Descriptor K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene Recognition, PAMI, 2010 http://koen.me/research/colordescriptors/ Code Image Segmentation Entropy Rate Superpixel Segmentation M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011 http://www.umiacs.umd.edu/~mingyliu/src/ers_matlab_wrapper_v0.1.zip Code Image Filtering Domain Transformation E. Gastal, M. Oliveira, Domain Transform for Edge-Aware Image and Video Processing, SIGGRAPH 2011 http://inf.ufrgs.br/~eslgastal/DomainTransform/DomainTransformFilters-Source-v1.0.zip Code Multiple Kernel Learning OpenKernel.org F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011 http://www.openkernel.org/ Code Image Segmentation Efficient Graph-based Image Segmentation - Matlab Wrapper P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004 http://www.mathworks.com/matlabcentral/fileexchange/25866-efficient-graph-based-image-segmentation Code Image Segmentation Biased Normalized Cut S. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut, CVPR 2011 http://www.cs.berkeley.edu/~smaji/projects/biasedNcuts/ Code Stereo Constant-Space Belief Propagation Q. Yang, L. Wang, and N. Ahuja, A Constant-Space Belief Propagation Algorithm for Stereo Matching, CVPR 2010 http://www.cs.cityu.edu.hk/~qiyang/publications/code/cvpr-10-csbp/csbp.htm Code Feature Detection andFeature Extraction Speeded Up Robust Feature (SURF) - Open SURF H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006 http://www.chrisevansdev.com/computer-vision-opensurf.html Code Visual Tracking Online boosting trackers H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR, 2006 http://www.vision.ee.ethz.ch/boostingTrackers/ Code Image Denoising Sparsity-based Image Denoising W. Dong, X. Li, L. Zhang and G. Shi, Sparsity-based Image Denoising vis Dictionary Learning and Structural Clustering, CVPR, 2011 http://www.csee.wvu.edu/~xinl/CSR.html Code Feature Detection andFeature Extraction Scale-invariant feature transform (SIFT) - VLFeat D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. http://www.vlfeat.org/ Code Clustering Spectral Clustering - UW Project   http://www.stat.washington.edu/spectral/ Code Image Deblurring Analyzing spatially varying blur A. Chakrabarti, T. Zickler, and W. T. Freeman, Analyzing Spatially-varying Blur, CVPR 2010 http://www.eecs.harvard.edu/~ayanc/svblur/ Code Multiple Instance Learning DD-SVM Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 2004   Code Feature Extraction GIST Descriptor A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, IJCV, 2001 http://people.csail.mit.edu/torralba/code/spatialenvelope/ Code Image Classification Texture Classification M. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005 http://www.robots.ox.ac.uk/~vgg/research/texclass/index.html Code Structure from motion Nonrigid Structure From Motion in Trajectory Space   http://cvlab.lums.edu.pk/nrsfm/index.html Code Alpha Matting Shared Matting E. S. L. Gastal and M. M. Oliveira, Computer Graphics Forum, 2010 http://www.inf.ufrgs.br/~eslgastal/SharedMatting/ Code Action Recognition 3D Gradients (HOG3D) A. Klaser, M. Marszałek, and C. Schmid, BMVC, 2008. http://lear.inrialpes.fr/people/klaeser/research_hog3d Code Image Denoising Kernel Regressions   http://www.soe.ucsc.edu/~htakeda/MatlabApp/KernelRegressionBasedImageProcessingToolBox_ver1-1beta.zip Code Feature Detection Boundary Preserving Dense Local Regions J. Kim and K. Grauman, Boundary Preserving Dense Local Regions, CVPR 2011 http://vision.cs.utexas.edu/projects/bplr/bplr.html Code Image Understanding SuperParsing J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric ImageParsing with Superpixels, ECCV 2010 http://www.cs.unc.edu/~jtighe/Papers/ECCV10/eccv10-jtighe-code.zip Code Image Filtering Weighted Least Squares Filter Z. Farbman, R. Fattal, D. Lischinski, R. Szeliski, Edge-Preserving Decompositions for Multi-Scale Tone and Detail Manipulation, SIGGRAPH 2008 http://www.cs.huji.ac.il/~danix/epd/ Code Image Super-resolution Single-Image Super-Resolution Matlab Package R. Zeyde, M. Elad, and M. Protter, On Single Image Scale-Up using Sparse-Representations, LNCS 2010 http://www.cs.technion.ac.il/~elad/Various/Single_Image_SR.zip Code Image Understanding Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics A. Gupta, A. A. Efros, M. Hebert, Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics, ECCV 2010 http://www.cs.cmu.edu/~abhinavg/blocksworld/#downloads Code Feature Extraction Shape Context S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002 http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sc_digits.html Code Image Processing andImage Filtering Piotr's Image & Video Matlab Toolbox Piotr Dollar, Piotr's Image & Video Matlab Toolbox, http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html Code Illumination, Reflectance, and Shadow Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse Sequences J-F. Lalonde, A. A. Efros, S. G. Narasimhan, Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse Sequences, SIGGRAPH Asia 2009 http://www.cs.cmu.edu/~jlalonde/software.html#skyModel Code Pose Estimation Calvin Upper-Body Detector E. Marcin, F. Vittorio, Better Appearance Models for Pictorial Structures, BMVC 2009 http://www.vision.ee.ethz.ch/~calvin/calvin_upperbody_detector/ Code Image Classification Locality-constrained Linear Coding J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification, CVPR, 2010 http://www.ifp.illinois.edu/~jyang29/LLC.htm Code Feature Detection andFeature Extraction Speeded Up Robust Feature (SURF) - Matlab Wrapper H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006 http://www.maths.lth.se/matematiklth/personal/petter/surfmex.php Code Pose Estimation Estimating Human Pose from Occluded Images J.-B. Huang and M.-H. Yang, Estimating Human Pose from Occluded Images, ACCV 2009 http://faculty.ucmerced.edu/mhyang/code/accv09_pose.zip Code Structure from motion OpenSourcePhotogrammetry   http://opensourcephotogrammetry.blogspot.com/ Code Image Classification Spatial Pyramid Matching S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, CVPR 2006 http://www.cs.unc.edu/~lazebnik/research/SpatialPyramid.zip Code Nearest Neighbors Matching Coherency Sensitive Hashing S. Korman, S. Avidan, Coherency Sensitive Hashing, ICCV 2011 http://www.eng.tau.ac.il/~simonk/CSH/index.html Code Image Segmentation Segmentation by Minimum Code Length A. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression, CVIU, 2007 http://perception.csl.uiuc.edu/coding/image_segmentation/ Code Saliency Detection Frequency-tuned salient region detection R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009 http://ivrgwww.epfl.ch/supplementary_material/RK_CVPR09/index.html Code MRF Optimization Max-flow/min-cut for shape fitting V. Lempitsky and Y. Boykov, Global Optimization for Shape Fitting, CVPR 2007 http://www.csd.uwo.ca/faculty/yuri/Implementations/TouchExpand.zip Code Feature Detection Canny Edge Detection J. Canny, A Computational Approach To Edge Detection, PAMI, 1986 http://www.mathworks.com/help/toolbox/images/ref/edge.html Code Object Detection Multiple Kernels A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection. ICCV, 2009 http://www.robots.ox.ac.uk/~vgg/software/MKL/ Code Image Segmentation Mean-Shift Image Segmentation - EDISON D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002 http://coewww.rutgers.edu/riul/research/code/EDISON/index.html Code Image Quality Assessment Degradation Model   http://users.ece.utexas.edu/~bevans/papers/2000/imageQuality/index.html Code Object Detection Ensemble of Exemplar-SVMs T. Malisiewicz, A. Gupta, A. Efros. Ensemble of Exemplar-SVMs for Object Detection and Beyond . ICCV, 2011 http://www.cs.cmu.edu/~tmalisie/projects/iccv11/ Code Image Deblurring Radon Transform T. S. Cho, S. Paris, B. K. P. Horn, W. T. Freeman, Blur kernel estimation using the radon transform, CVPR 2011 http://people.csail.mit.edu/taegsang/Documents/RadonDeblurringCode.zip Code Image Deblurring Eficient Marginal Likelihood Optimization in Blind Deconvolution A. Levin, Y. Weiss, F. Durand, W. T. Freeman. Efficient Marginal Likelihood Optimization in Blind Deconvolution, CVPR 2011 http://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR2011Code.zip Code Feature Detection FAST Corner Detection E. Rosten and T. Drummond, Machine learning for high-speed corner detection, ECCV, 2006 http://www.edwardrosten.com/work/fast.html Code Image Super-resolution MDSP Resolution Enhancement Software S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Fast and Robust Multi-frame Super-resolution, TIP 2004 http://users.soe.ucsc.edu/~milanfar/software/superresolution.html Code Feature Extraction andObject Detection Histogram of Oriented Graidents - INRIA Object Localization Toolkit N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005 http://www.navneetdalal.com/software Code Visual Tracking Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects H. Pirsiavash, D. Ramanan, C. Fowlkes. "Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects, CVPR 2011 http://www.ics.uci.edu/~hpirsiav/papers/tracking_cvpr11_release_v1.0.tar.gz Code Saliency Detection Segmenting salient objects from images and videos E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos. CVPR, 2010 http://www.cse.oulu.fi/MVG/Downloads/saliency Code Visual Tracking Object Tracking A. Yilmaz, O. Javed and M. Shah, Object Tracking: A Survey, ACM Journal of Computing Surveys, Vol. 38, No. 4, 2006 http://plaza.ufl.edu/lvtaoran/object%20tracking.htm Code Machine Learning Boosting Resources by Liangliang Cao http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm Code Machine Learning Netlab Neural Network Software C. M. Bishop, Neural Networks for Pattern RecognitionㄝOxford University Press, 1995 http://www1.aston.ac.uk/eas/research/groups/ncrg/resources/netlab/ Code Optical Flow Classical Variational Optical Flow T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, ECCV 2004 http://lmb.informatik.uni-freiburg.de/resources/binaries/ Code Sparse Representation Centralized Sparse Representation for Image Restoration W. Dong, L. Zhang and G. Shi, “Centralized Sparse Representation for Image Restoration,” ICCV 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/CSR_IR.zip Course Computer Vision Introduction to Computer Vision, Stanford University, Winter 2010-2011 Fei-Fei Li http://vision.stanford.edu/teaching/cs223b/ Course Computer Vision Computer Vision: From 3D Reconstruction to Visual Recognition, Fall 2012 Silvio Savarese and Fei-Fei Li https://www.coursera.org/course/computervision Course Computer Vision Computer Vision, University of Texas at Austin, Spring 2011 Kristen Grauman http://www.cs.utexas.edu/~grauman/courses/spring2011/index.html Course Computer Vision Learning-Based Methods in Vision, CMU, Spring 2012 Alexei “Alyosha” Efros and Leonid Sigal https://docs.google.com/document/pub?id=1jGBn7zPDEaU33fJwi3YI_usWS-U6gpSSJotV_2gDrL0 Course Visual Recognition Visual Recognition, University of Texas at Austin, Fall 2011 Kristen Grauman http://www.cs.utexas.edu/~grauman/courses/fall2011/schedule.html Course Computer Vision Introduction to Computer Vision James Hays, Brown University, Fall 2011 http://www.cs.brown.edu/courses/cs143/ Course Computer Vision Computer Vision, University of North Carolina at Chapel Hill, Spring 2010 Svetlana Lazebnik http://www.cs.unc.edu/~lazebnik/spring10/ Course Computer Vision Computer Vision: The Fundamentals, University of California at Berkeley, Fall 2012 Jitendra Malik https://www.coursera.org/course/vision Course Computational Photography Computational Photography, University of Illinois, Urbana-Champaign, Fall 2011 Derek Hoiem http://www.cs.illinois.edu/class/fa11/cs498dh/ Course Graphical Models Inference in Graphical Models, Stanford University, Spring 2012 Andrea Montanari, Stanford University http://www.stanford.edu/~montanar/TEACHING/Stat375/stat375.html Course Computer Vision Computer Vision, New York University, Fall 2012 Rob Fergus http://cs.nyu.edu/~fergus/teaching/vision_2012/index.html Course Computer Vision Advances in Computer Vision Antonio Torralba, MIT, Spring 2010 http://groups.csail.mit.edu/vision/courses/6.869/ Course Computer Vision Computer Vision, University of Illinois, Urbana-Champaign, Spring 2012 Derek Hoiem http://www.cs.illinois.edu/class/sp12/cs543/ Course Computational Photography Computational Photography, CMU, Fall 2011 Alexei “Alyosha” Efros http://graphics.cs.cmu.edu/courses/15-463/2011_fall/463.html Course Computer Vision Computer Vision, University of Washington, Winter 2012 Steven Seitz http://www.cs.washington.edu/education/courses/cse455/12wi/ Link Source code Source Code Collection for Reproducible Research collected by Xin Li, Lane Dept of CSEE, West Virginia University http://www.csee.wvu.edu/~xinl/reproducible_research.html Link Computer Vision Computer Image Analysis, Computer Vision Conferences USC http://iris.usc.edu/information/Iris-Conferences.html Link Computer Vision CV Papers on the web CVPapers http://www.cvpapers.com/index.html Link Computer Vision CVonline CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision http://homepages.inf.ed.ac.uk/rbf/CVonline/ Link Dataset Compiled list of recognition datasets compiled by Kristen Grauman http://www.cs.utexas.edu/~grauman/courses/spring2008/datasets.htm Link Computer Vision Annotated Computer Vision Bibliography compiled by Keith Price http://iris.usc.edu/Vision-Notes/bibliography/contents.html Link Computer Vision The Computer Vision homepage   http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html Link Computer Vision Industry The Computer Vision Industry David Lowe http://www.cs.ubc.ca/~lowe/vision.html Link Source code Computer Vision Algorithm Implementations CVPapers http://www.cvpapers.com/rr.html Link Computer Vision CV Datasets on the web CVPapers http://www.cvpapers.com/datasets.html Talk Visual Recognition Understanding Visual Scenes Antonio Torralba, MIT http://videolectures.net/nips09_torralba_uvs/ Talk Neuroscience Learning in Hierarchical Architectures: from Neuroscience to Derived Kernels Tomaso A. Poggio, McGovern Institute for Brain Research, Massachusetts Institute of Technology http://videolectures.net/mlss09us_poggio_lhandk/ Talk Deep Learning A tutorial on Deep Learning Geoffrey E. Hinton, Department of Computer Science, University of Toronto http://videolectures.net/jul09_hinton_deeplearn/ Talk Boosting Theory and Applications of Boosting Robert Schapire, Department of Computer Science, Princeton University http://videolectures.net/mlss09us_schapire_tab/ Talk Graphical Models Graphical Models and message-passing algorithms Martin J. Wainwright, University of California at Berkeley http://videolectures.net/mlss2011_wainwright_messagepassing/ Talk Statistical Learning Theory Statistical Learning Theory John Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College London http://videolectures.net/mlss04_taylor_slt/ Talk Gaussian Process Gaussian Process Basics David MacKay, University of Cambridge http://videolectures.net/gpip06_mackay_gpb/ Talk Information Theory Information Theory David MacKay, University of Cambridge http://videolectures.net/mlss09uk_mackay_it/ Talk Optimization Optimization Algorithms in Machine Learning Stephen J. Wright, Computer Sciences Department, University of Wisconsin - Madison http://videolectures.net/nips2010_wright_oaml/ Talk Bayesian Inference Introduction To Bayesian Inference Christopher Bishop, Microsoft Research http://videolectures.net/mlss09uk_bishop_ibi/ Talk Bayesian Nonparametrics Modern Bayesian Nonparametrics Peter Orbanz and Yee Whye Teh http://www.youtube.com/watch?v=F0_ih7THV94&feature=relmfu Talk Kernels and Distances Machine learning and kernel methods for computer vision Francis R. Bach, INRIA http://videolectures.net/etvc08_bach_mlakm/ Talk Optimization Convex Optimization Lieven Vandenberghe, Electrical Engineering Department, University of California, Los Angeles http://videolectures.net/mlss2011_vandenberghe_convex/ Talk Optimization Energy Minimization with Label costs and Applications in Multi-Model Fitting Yuri Boykov, Department of Computer Science, University of Western Ontario http://videolectures.net/nipsworkshops2010_boykov_eml/ Talk Object Detection Object Recognition with Deformable Models Pedro Felzenszwalb, Brown University http://www.youtube.com/watch?v=_J_clwqQ4gI Talk Low-level vision Learning and Inference in Low-Level Vision Yair Weiss, School of Computer Science and Engineering, The Hebrew University of Jerusalem http://videolectures.net/nips09_weiss_lil/ Talk 3D Computer Vision 3D Computer Vision: Past, Present, and Future Steven Seitz, University of Washington, Google Tech Talk, 2011 http://www.youtube.com/watch?v=kyIzMr917Rc Talk Optimization Who is Afraid of Non-Convex Loss Functions? Yann LeCun, New York University http://videolectures.net/eml07_lecun_wia/ Talk Sparse Representation Sparse Methods for Machine Learning: Theory and Algorithms Francis R. Bach, INRIA http://videolectures.net/nips09_bach_smm/ Talk Optimization and Support Vector Machines Optimization Algorithms in Support Vector Machines Stephen J. Wright, Computer Sciences Department, University of Wisconsin - Madison http://videolectures.net/mlss09us_wright_oasvm/ Talk Information Theory Information Theory in Learning and Control Naftali (Tali) Tishby, The Hebrew University http://www.youtube.com/watch?v=GKm53xGbAOk&feature=relmfu Talk Relative Entropy Relative Entropy Sergio Verdu, Princeton University http://videolectures.net/nips09_verdu_re/ Tutorial Object Detection Geometry constrained parts based detection Simon Lucey, Jason Saragih, ICCV 2011 Tutorial http://ci2cv.net/tutorials/iccv-2011/ Tutorial Graphical Models Learning with inference for discrete graphical models Nikos Komodakis, Pawan Kumar, Nikos Paragios, Ramin Zabih, ICCV 2011 Tutorial http://www.csd.uoc.gr/~komod/ICCV2011_tutorial/ Tutorial Variational Calculus Variational methods for computer vision Daniel Cremers, Bastian Goldlucke, Thomas Pock, ICCV 2011 Tutorial http://cvpr.in.tum.de/tutorials/iccv2011 Tutorial 3D perception Computer Vision and 3D Perception for Robotics Radu Bogdan Rusu, Gary Bradski, Caroline Pantofaru, Stefan Hinterstoisser, Stefan Holzer, Kurt Konolige and Andrea Vedaldi, ECCV 2010 Tutorial http://www.willowgarage.com/workshops/2010/eccv Tutorial Action Recognition Looking at people: The past, the present and the future L. Sigal, T. Moeslund, A. Hilton, V. Kruger, ICCV 2011 Tutorial http://www.cs.brown.edu/~ls/iccv2011tutorial.html Tutorial Non-linear Least Squares Computer vision fundamentals: robust non-linear least-squares and their applications Pascal Fua, Vincent Lepetit, ICCV 2011 Tutorial http://cvlab.epfl.ch/~fua/courses/lsq/ Tutorial Action Recognition Frontiers of Human Activity Analysis J. K. Aggarwal, Michael S. Ryoo, and Kris Kitani, CVPR 2011 Tutorial http://cvrc.ece.utexas.edu/mryoo/cvpr2011tutorial/ Tutorial Structured Prediction Structured Prediction and Learning in Computer Vision S. Nowozin and C. Lampert, CVPR 2011 Tutorial http://www.nowozin.net/sebastian/cvpr2011tutorial/ Tutorial Action Recognition Statistical and Structural Recognition of Human Actions Ivan Laptev and Greg Mori, ECCV 2010 Tutorial https://sites.google.com/site/humanactionstutorialeccv10/ Tutorial Computational Symmetry Computational Symmetry: Past, Current, Future Yanxi Liu, ECCV 2010 Tutorial http://vision.cse.psu.edu/research/symmComp/index.shtml Tutorial Matlab Matlab Tutorial David Kriegman and Serge Belongie http://www.cs.unc.edu/~lazebnik/spring10/matlab.intro.html Tutorial Matlab Writing Fast MATLAB Code Pascal Getreuer, Yale University http://www.mathworks.com/matlabcentral/fileexchange/5685 Tutorial Spectral Clustering A Tutorial on Spectral Clustering Ulrike von Luxburg, Max Planck Institute for Biological Cybernetics http://web.mit.edu/~wingated/www/introductions/tutorial_on_spectral_clustering.pdf Tutorial Feature Learning, Image Classification Feature Learning for Image Classification Kai Yu and Andrew Ng, ECCV 2010 Tutorial http://ufldl.stanford.edu/eccv10-tutorial/ Tutorial Shape Analysis, Diffusion Geometry Diffusion Geometry Methods in Shape Analysis A. Brontein and M. Bronstein, ECCV 2010 Tutorial http://tosca.cs.technion.ac.il/book/course_eccv10.html Tutorial Graphical Models Graphical Models, Exponential Families, and Variational Inference Martin J. Wainwright and Michael I. Jordan, University of California at Berkeley http://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf Tutorial Color Image Processing Color image understanding: from acquisition to high-level image understanding Theo Gevers, Keigo Hirakawa, Joost van de Weijer, ICCV 2011 Tutorial http://www.cat.uab.cat/~joost/tutorial_iccv.html Tutorial Structure from motion Nonrigid Structure from Motion Y. Sheikh and Sohaib Khan, ECCV 2010 Tutorial http://www.cs.cmu.edu/~yaser/ECCV2010Tutorial.html Tutorial Expectation Maximization A Gentle Tutorial of the EM Algorithmand its Application to ParameterEstimation for Gaussian Mixture andHidden Markov Models Jeff A. Bilmes, University of California at Berkeley http://crow.ee.washington.edu/people/bulyko/papers/em.pdf Tutorial Decision Forests Decision forests for classification, regression, clustering and density estimation A. Criminisi, J. Shotton and E. Konukoglu, ICCV 2011 Tutorial http://research.microsoft.com/en-us/groups/vision/decisionforests.aspx Tutorial 3D point cloud processing 3D point cloud processing: PCL (Point Cloud Library) R. Rusu, S. Holzer, M. Dixon, V. Rabaud, ICCV 2011 Tutorial http://www.pointclouds.org/media/iccv2011.html Tutorial Image Registration Tools and Methods for Image Registration 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 http://www.imgfsr.com/CVPR2011/Tutorial6/ Tutorial Non-rigid registration Non-rigid registration and reconstruction Alessio Del Bue, Lourdes Agapito, Adrien Bartoli, ICCV 2011 Tutorial http://www.isr.ist.utl.pt/~adb/tutorial/ Tutorial Variational Calculus Variational Methods in Computer Vision D. Cremers, B. Goldlücke, T. Pock, ECCV 2010 Tutorial http://cvpr.cs.tum.edu/tutorials/eccv2010 Tutorial Distance Metric Learning Distance Functions and Metric Learning M. Werman, O. Pele and B. Kulis, ECCV 2010 Tutorial http://www.cs.huji.ac.il/~ofirpele/DFML_ECCV2010_tutorial/ Tutorial Feature Extraction Image and Video Description with Local Binary Pattern Variants M. Pietikainen and J. Heikkila, CVPR 2011 Tutorial http://www.ee.oulu.fi/research/imag/mvg/files/pdf/CVPR-tutorial-final.pdf Tutorial Game Theory Game Theory in Computer Vision and Pattern Recognition Marcello Pelillo and Andrea Torsello, CVPR 2011 Tutorial http://www.dsi.unive.it/~atorsell/cvpr2011tutorial/ Tutorial Computational Imaging Fcam: an architecture and API for computational cameras Kari Pulli, Andrew Adams, Timo Ahonen, Marius Tico, ICCV 2011 Tutorial http://fcam.garage.maemo.org/iccv2011.html

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

Conference Information	Computer Image Analysis, Computer Vision Conferences
Papers	Computer vision paper on the web NIPS Proceedings
Datasets	Compiled list of recognition datasets The PASCAL Visual Object Classes Computer vision dataset from CMU
Lectures	Videolectures
Source Codes	Computer Vision Algorithm Implementations OpenCV Source Code Collection for Reproducible Research
Patents	United States Patent & Trademark Office
Source Codes	Computer Vision Algorithm Implementations OpenCV Source Code Collection for Reproducible Research