【更新】Jia-Bin Huang收集的计算机视觉代码库

维护地址为： https://sites.google.com/site/jbhuang0604/resources/vision

内容详实，作为入门领域的参考非常有用！

	label	topic	type	resource_url:url	reference
	Scale-invariant feature transform (SIFT) - Demo Software	Feature Detection; Feature Extraction	Code	http://www.cs.ubc.ca/~lowe/keypoints/	D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.
	Scale-invariant feature transform (SIFT) - Library	Feature Detection; Feature Extraction	Code	http://blogs.oregonstate.edu/hess/code/sift/	D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.
	Scale-invariant feature transform (SIFT) - VLFeat	Feature Detection; Feature Extraction	Code	http://www.vlfeat.org/	D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004.
	Normalized Cut	Image Segmentation	Code	http://www.cis.upenn.edu/~jshi/software/	J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000
	Discriminatively Trained Deformable Part Models	Object Detection	Code	http://people.cs.uchicago.edu/~pff/latent/	P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models, PAMI, 2010
	PCA-SIFT	Feature Extraction	Code	http://www.cs.cmu.edu/~yke/pcasift/	Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004
	Affine-SIFT	Feature Detection; Feature Extraction	Code	http://www.ipol.im/pub/algo/my_affine_sift/	J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009
	Speeded Up Robust Feature (SURF) - Open SURF	Feature Detection; Feature Extraction	Code	http://www.chrisevansdev.com/computer-vision-opensurf.html	H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006
	Speeded Up Robust Feature (SURF) - Matlab Wrapper	Feature Detection; Feature Extraction	Code	http://www.maths.lth.se/matematiklth/personal/petter/surfmex.php	H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust Features, ECCV, 2006
	Maximally stable extremal regions (MSER)	Feature Detection; Feature Extraction	Code	http://www.robots.ox.ac.uk/~vgg/research/affine/	J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002
	Maximally stable extremal regions (MSER) - VLFeat	Feature Detection; Feature Extraction	Code	http://www.vlfeat.org/	J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002
	Geometric Blur	Feature Detection; Feature Extraction	Code	http://www.robots.ox.ac.uk/~vgg/software/MKL/	A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR, 2005
	Local Self-Similarity Descriptor	Feature Extraction	Code	http://www.robots.ox.ac.uk/~vgg/software/SelfSimilarity/	E. Shechtman and M. Irani. Matching local self-similarities across images and videos, CVPR, 2007
	Global and Efficient Self-Similarity	Feature Extraction	Code	http://www.vision.ee.ethz.ch/~calvin/gss/selfsim_release1.0.tgz	T. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection. CVPR 2010
	Histogram of Oriented Graidents - INRIA Object Localization Toolkit	Feature Extraction; Object Detection	Code	http://www.navneetdalal.com/software	N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005
	Histogram of Oriented Graidents - OLT for windows	Feature Extraction; Object Detection	Code	http://www.computing.edu.au/~12482661/hog.html	N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005
	GIST Descriptor	Feature Extraction	Code	http://people.csail.mit.edu/torralba/code/spatialenvelope/	A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope, IJCV, 2001
	Shape Context	Feature Extraction	Code	http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sc_digits.html	S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contexts, PAMI, 2002
	Color Descriptor	Feature Detection; Feature Extraction	Code	http://koen.me/research/colordescriptors/	K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene Recognition, PAMI, 2010
	Pyramids of Histograms of Oriented Gradients (PHOG)	Feature Extraction	Code	http://www.robots.ox.ac.uk/~vgg/research/caltech/phog/phog.zip	A. Bosch, A. Zisserman, and X. Munoz, Representing shape with a spatial pyramid kernel, CIVR, 2007
	Space-Time Interest Points (STIP)	Feature Detection; Feature Extraction	Code	http://www.irisa.fr/vista/Equipe/People/Laptev/download/stip-1.1-winlinux.zip	I. Laptev, On Space-Time Interest Points, IJCV, 2005
	Boundary Preserving Dense Local Regions	Feature Detection	Code	http://vision.cs.utexas.edu/projects/bplr/bplr.html	J. Kim and K. Grauman, Boundary Preserving Dense Local Regions, CVPR 2011
	Canny Edge Detection	Feature Detection	Code	http://www.mathworks.com/help/toolbox/images/ref/edge.html	J. Canny, A Computational Approach To Edge Detection, PAMI, 1986
	FAST Corner Detection	Feature Detection	Code	http://www.edwardrosten.com/work/fast.html	E. Rosten and T. Drummond, Machine learning for high-speed corner detection, ECCV, 2006
	Affine Covariant Features	Feature Detection; Feature Extraction	Code	http://www.robots.ox.ac.uk/~vgg/research/affine/	T. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors: A Survey, Foundations and Trends in Computer Graphics and Vision, 2008
	Groups of Adjacent Contour Segments	Feature Detection; Feature Extraction	Code	http://www.robots.ox.ac.uk/~vgg/share/ferrari/release-kas-v102.tgz	V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, Groups of Adjacent Contour Segments for Object Detection, PAMI, 2007
	Superpixel by Gerg Mori	Image Segmentation	Code	http://www.cs.sfu.ca/~mori/research/superpixels/	X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003
	Efficient Graph-based Image Segmentation - C++ code	Image Segmentation	Code	http://people.cs.uchicago.edu/~pff/segment/	P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004
	Efficient Graph-based Image Segmentation - Matlab Wrapper	Image Segmentation	Code	http://www.mathworks.com/matlabcentral/fileexchange/25866-efficient-graph-based-image-segmentation	P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004
	Mean-Shift Image Segmentation - EDISON	Image Segmentation	Code	http://coewww.rutgers.edu/riul/research/code/EDISON/index.html	D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002
	Mean-Shift Image Segmentation - Matlab Wrapper	Image Segmentation	Code	http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/edison_matlab_interface.tar.gz	D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 2002
	OWT-UCM Hierarchical Segmentation	Image Segmentation	Code	http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html	P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011
	Turbepixels	Image Segmentation	Code	http://www.cs.toronto.edu/~babalex/research.html	A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric Flows, PAMI 2009
	Quick-Shift	Image Segmentation	Code	http://www.vlfeat.org/overview/quickshift.html	A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking, ECCV, 2008
	SLIC Superpixels	Image Segmentation	Code	http://ivrg.epfl.ch/supplementary_material/RK_SLICSuperpixels/index.html	R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010
	Segmentation by Minimum Code Length	Image Segmentation	Code	http://perception.csl.uiuc.edu/coding/image_segmentation/	A. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data Compression, CVIU, 2007
	Biased Normalized Cut	Image Segmentation	Code	http://www.cs.berkeley.edu/~smaji/projects/biasedNcuts/	S. Maji, N. Vishnoi and J. Malik, Biased Normalized Cut, CVPR 2011
	Multiscale Segmentation Tree	Image Segmentation	Code	http://vision.ai.uiuc.edu/segmentation	E. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,” ACCV 2009; N. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996
	Entropy Rate Superpixel Segmentation	Image Segmentation	Code	http://www.umiacs.umd.edu/~mingyliu/src/ers_matlab_wrapper_v0.1.zip	M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011
	Viola-Jones Object Detection	Object Detection	Code	http://pr.willowgarage.com/wiki/FaceDetection	P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features, CVPR, 2001
	A simple object detector with boosting	Object Detection	Code	http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html	ICCV 2005 short courses on Recognizing and Learning Object Categories
	Cascade Object Detection with Deformable Part Models	Object Detection	Code	http://people.cs.uchicago.edu/~rbg/star-cascade/	P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models. CVPR, 2010
	Poselet	Object Detection	Code	http://www.eecs.berkeley.edu/~lbourdev/poselets/	L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009
	Implicit Shape Model	Object Detection	Code	http://www.vision.ee.ethz.ch/~bleibe/code/ism.html	B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and Segmentation, IJCV, 2008
	A simple parts and structure object detector	Object Detection	Code	http://people.csail.mit.edu/fergus/iccv2005/partsstructure.html	ICCV 2005 short courses on Recognizing and Learning Object Categories
	Max-Margin Hough Transform	Object Detection	Code	http://www.cs.berkeley.edu/~smaji/projects/max-margin-hough/	S. Maji and J. Malik, Object Detection Using a Max-Margin Hough Transform. CVPR 2009
	Ensemble of Exemplar-SVMs	Object Detection	Code	http://www.cs.cmu.edu/~tmalisie/projects/iccv11/	T. Malisiewicz, A. Gupta, A. Efros. Ensemble of Exemplar-SVMs for Object Detection and Beyond . ICCV, 2011
	Recognition using regions	Object Detection	Code	http://www.cs.berkeley.edu/~chunhui/publications/cvpr09_v2.zip	C. Gu, J. J. Lim, P. Arbelaez, and J. Malik, CVPR 2009
	Closed Form Matting	Alpha Matting	Code	http://people.csail.mit.edu/alevin/matting.tar.gz	A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting, PAMI 2008.
	Spectral Matting	Alpha Matting	Code	http://www.vision.huji.ac.il/SpectralMatting/	A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008
	Learning-based Matting	Alpha Matting	Code	http://www.mathworks.com/matlabcentral/fileexchange/31412	Y. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009
	Bayesian Matting	Alpha Matting	Code	http://www1.idc.ac.il/toky/CompPhoto-09/Projects/Stud_projects/Miki/index.html	Y. Y. Chuang, B. Curless, D. H. Salesin, and R. Szeliski, A Bayesian Approach to Digital Matting, CVPR, 2001
	Shared Matting	Alpha Matting	Code	http://www.inf.ufrgs.br/~eslgastal/SharedMatting/	E. S. L. Gastal and M. M. Oliveira, Computer Graphics Forum, 2010
	Fast Bilateral Filter	Image Filtering	Code	http://people.csail.mit.edu/sparis/bf/	S. Paris and F. Durand, A Fast Approximation of the Bilateral Filter using a Signal Processing Approach, ECCV, 2006
	Weighted Least Squares Filter	Image Filtering	Code	http://www.cs.huji.ac.il/~danix/epd/	Z. Farbman, R. Fattal, D. Lischinski, R. Szeliski, Edge-Preserving Decompositions for Multi-Scale Tone and Detail Manipulation, SIGGRAPH 2008
	Domain Transformation	Image Filtering	Code	http://inf.ufrgs.br/~eslgastal/DomainTransform/DomainTransformFilters-Source-v1.0.zip	E. Gastal, M. Oliveira, Domain Transform for Edge-Aware Image and Video Processing, SIGGRAPH 2011
	Local Laplacian Filters	Image Filtering	Code	http://people.csail.mit.edu/sparis/publi/2011/siggraph/matlab_source_code.zip	S. Paris, S. Hasinoff, J. Kautz, Local Laplacian Filters: Edge-Aware Image Processing with a Laplacian Pyramid, SIGGRAPH 2011
	Image smoothing via L0 Gradient Minimization	Image Filtering	Code	http://www.cse.cuhk.edu.hk/~leojia/projects/L0smoothing/L0smoothing.zip	L. Xu, C. Lu, Y. Xu, J. Jia, Image smoothing via L0 Gradient Minimization, SIGGRAPH Asia 2011
	Guided Image Filtering	Image Filtering	Code	http://personal.ie.cuhk.edu.hk/~hkm007/eccv10/guided-filter-code-v1.rar	K. He, J. Sun, X. Tang, Guided Image Filtering, ECCV 2010
	Anisotropic Diffusion	Image Filtering	Code	http://www.mathworks.com/matlabcentral/fileexchange/14995-anisotropic-diffusion-perona-malik	P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, PAMI 1990
	Real-time O(1) Bilateral Filtering	Image Filtering	Code	http://vision.ai.uiuc.edu/~qyang6/publications/code/qx_constant_time_bilateral_filter_ss.zip	Q. Yang, K.-H. Tan and N. Ahuja, Real-time O(1) Bilateral Filtering,  CVPR 2009
	SVM for Edge-Preserving Filtering	Image Filtering	Code	http://vision.ai.uiuc.edu/~qyang6/publications/code/cvpr-10-svmbf/program_video_conferencing.zip	Q. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering,  CVPR 2010
	Edge Foci Interest Points	Feature Detection	Code	http://research.microsoft.com/en-us/um/people/larryz/edgefoci/edge_foci.htm	L. Zitnickand K. Ramnath, Edge Foci Interest Points, ICCV, 2011
	K-SVD	Image Denoising	Code	http://www.cs.technion.ac.il/~ronrubin/Software/ksvdbox13.zip
	BLS-GSM	Image Denoising	Code	http://decsai.ugr.es/~javier/denoise/
	BM3D	Image Denoising	Code	http://www.cs.tut.fi/~foi/GCF-BM3D/
	Field of Experts	Image Denoising	Code	http://www.cs.brown.edu/~roth/research/software.html
	Gaussian Field of Experts	Image Denoising	Code	http://www.cs.huji.ac.il/~yweiss/BRFOE.zip
	Non-local Means	Image Denoising	Code	http://dmi.uib.es/~abuades/codis/NLmeansfilter.m
	Kernel Regressions	Image Denoising	Code	http://www.soe.ucsc.edu/~htakeda/MatlabApp/KernelRegressionBasedImageProcessingToolBox_ver1-1beta.zip
	Efficient Belief Propagation for Early Vision	Image Denoising; Stereo Matching	Code	http://www.cs.brown.edu/~pff/bp/	P. F. Felzenszwalb and D. P. Huttenlocher, Efficient Belief Propagation for Early Vision, IJCV, 2006
	Clustering-based Denoising	Image Denoising	Code	http://users.soe.ucsc.edu/~priyam/K-LLD/	P. Chatterjee and P. Milanfar, Clustering-based Denoising with Locally Learned Dictionaries (K-LLD), TIP, 2009
	Sparsity-based Image Denoising	Image Denoising	Code	http://www.csee.wvu.edu/~xinl/CSR.html	W. Dong, X. Li, L. Zhang and G. Shi, Sparsity-based Image Denoising vis Dictionary Learning and Structural Clustering, CVPR, 2011
	Learning Models of Natural Image Patches	Image Denoising; Image Super-resolution; Image Deblurring	Code	http://www.cs.huji.ac.il/~daniez/	D. Zoran and Y. Weiss, From Learning Models of Natural Image Patches to Whole Image Restoration, ICCV, 2011
	Hough Forests for Object Detection	Object Detection	Code	http://www.vision.ee.ethz.ch/~gallju/projects/houghforest/index.html	J. Gall and V. Lempitsky, Class-Specific Hough Forests for Object Detection, CVPR, 2009
	Lucas-Kanade affine template tracking	Visual Tracking	Code	http://www.mathworks.com/matlabcentral/fileexchange/24677-lucas-kanade-affine-template-tracking	S. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, IJCV 2002
	EasyCamCalib	Camera Calibration	Code	http://arthronav.isr.uc.pt/easycamcalib/	J. Barreto, J. Roquette, P. Sturm, and F. Fonseca, Automatic camera calibration applied to medical endoscopy, BMVC, 2009
	3D Gradients (HOG3D)	Action Recognition	Code	http://lear.inrialpes.fr/people/klaeser/research_hog3d	A. Klaser, M. Marszałek, and C. Schmid, BMVC, 2008.
	Dense Trajectories Video Description	Action Recognition	Code	http://lear.inrialpes.fr/people/wang/dense_trajectories	H. Wang and A. Klaser and C. Schmid and C.- L. Liu, Action Recognition by Dense Trajectories, CVPR, 2011
	ClassCut for Unsupervised Class Segmentation	Object Segmentation	Code	http://www.vision.ee.ethz.ch/~calvin/classcut/ClassCut-release.zip	B. Alexe, T. Deselaers and V. Ferrari, ClassCut for Unsupervised Class Segmentation, ECCV 2010
	Global and Efficient Self-Similarity	Feature Extraction	Code	http://www.vision.ee.ethz.ch/~calvin/gss/selfsim_release1.0.tgz	T. Deselaers, V. Ferrari, Global and Efficient Self-Similarity for Object Classification and Detection, CVPR 2010
	Calvin Upper-Body Detector	Pose Estimation	Code	http://www.vision.ee.ethz.ch/~calvin/calvin_upperbody_detector/	E. Marcin, F. Vittorio, Better Appearance Models for Pictorial Structures, BMVC 2009
	Horn and Schunck's Optical Flow	Optical Flow	Code	http://www.cs.brown.edu/~dqsun/code/hs.zip
	Black and Anandan's Optical Flow	Optical Flow	Code	http://www.cs.brown.edu/~dqsun/code/ba.zip
	Optical Flow by Deqing Sun	Optical Flow	Code	http://www.cs.brown.edu/~dqsun/code/flow_code.zip	D. Sun, S. Roth, M. J. Black, Secrets of Optical Flow Estimation and Their Principles, CVPR, 2010
	L1 Tracking	Visual Tracking	Code	http://www.dabi.temple.edu/~hbling/code_data.htm	X. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009
	Efficient Earth Mover's Distance with L1 Ground Distance (EMD_L1)	Kernels and Distances	Code	http://www.dabi.temple.edu/~hbling/code/EmdL1_v3.zip	H. Ling and K. Okada, An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison, PAMI 2007
	Diffusion-based distance	Kernels and Distances	Code	http://www.dabi.temple.edu/~hbling/code/DD_v1.zip	H. Ling and K. Okada, Diffusion Distance for Histogram Comparison, CVPR 2006
	Particle Filter Object Tracking	Visual Tracking	Code	http://blogs.oregonstate.edu/hess/code/particles/
	GPU Implementation of Kanade-Lucas-Tomasi Feature Tracker	Visual Tracking	Code	http://cs.unc.edu/~ssinha/Research/GPU_KLT/	S. N Sinha, J.-M. Frahm, M. Pollefeys and Y. Genc, Feature Tracking and Matching in Video Using Programmable Graphics Hardware, MVA, 2007
	Space-Time Interest Points (STIP)	Feature Extraction; Action Recognition	Code	http://www.nada.kth.se/cvap/abstracts/cvap284.html	I. Laptev and T. Lindeberg, On Space-Time Interest Points, IJCV 2005
	KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker	Visual Tracking	Code	http://www.ces.clemson.edu/~stb/klt/	B. D. Lucas and T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. IJCAI, 1981
	Camera Calibration Toolbox for Matlab	Camera Calibration	Code	http://www.vision.caltech.edu/bouguetj/calib_doc/	http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/ref.html
	The Pyramid Match: Efficient Matching for Retrieval and Recognition	Feature Matching; Image Classification	Code	http://www.cs.utexas.edu/~grauman/research/projects/pmk/pmk_projectpage.htm	K. Grauman and T. Darrell. The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, ICCV 2005
	Piotr's Image & Video Matlab Toolbox	Image Processing; Image Filtering	Code	http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html	Piotr Dollar, Piotr's Image & Video Matlab Toolbox, http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html
	Epipolar Geometry Toolbox	Camera Calibration	Code	http://egt.dii.unisi.it/	G.L. Mariottini, D. Prattichizzo, EGT: a Toolbox for Multiple View Geometry and Visual Servoing, IEEE Robotics & Automation Magazine, 2005
	Matlab Functions for Multiple View Geometry	Multiple View Geometry	Code	http://www.robots.ox.ac.uk/~vgg/hzbook/code/
	MATLAB and Octave Functions  for Computer Vision and Image Processing	Multiple View Geometry	Code	http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.html	P. D. Kovesi. MATLAB and Octave Functions for Computer Vision and Image Processing, http://www.csse.uwa.edu.au/~pk/research/matlabfns
	Motion Tracking in Image Sequences	Visual Tracking	Code	http://www.cs.berkeley.edu/~flw/tracker/	C. Stauffer and W. E. L. Grimson. Learning patterns of activity using real-time tracking, PAMI, 2000
	Boosting Resources by Liangliang Cao	Machine Learning	Code	http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm	http://www.ifp.illinois.edu/~cao4/reading/boostingbib.htm
	Tracking with Online Multiple Instance Learning	Visual Tracking	Code	http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml	B. Babenko, M.-H. Yang, S. Belongie, Visual Tracking with Online Multiple Instance Learning, PAMI 2011
	Text recognition in the wild	Text Recognition	Code	http://vision.ucsd.edu/~kai/grocr/	K. Wang, B. Babenko, and S. Belongie, End-to-end Scene Text Recognition, ICCV 2011
	Object Tracking	Visual Tracking	Code	http://plaza.ufl.edu/lvtaoran/object%20tracking.htm	A. Yilmaz, O. Javed and M. Shah, Object Tracking: A Survey, ACM Journal of Computing Surveys, Vol. 38, No. 4, 2006
	Online boosting trackers	Visual Tracking	Code	http://www.vision.ee.ethz.ch/boostingTrackers/	H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR, 2006
	Kinect SDK	Depth Sensor	Code	http://www.microsoft.com/en-us/kinectforwindows/	http://www.microsoft.com/en-us/kinectforwindows/
	Statistical Pattern Recognition Toolbox	Machine Learning	Code	http://cmp.felk.cvut.cz/cmp/software/stprtool/	M.I. Schlesinger, V. Hlavac: Ten lectures on the statistical and structural pattern recognition, Kluwer Academic Publishers, 2002
	Netlab Neural Network Software	Machine Learning	Code	http://www1.aston.ac.uk/eas/research/groups/ncrg/resources/netlab/	C. M. Bishop, Neural Networks for Pattern RecognitionㄝOxford University Press, 1995
	FastICA package for MATLAB	Machine Learning	Code	http://research.ics.tkk.fi/ica/fastica/	http://research.ics.tkk.fi/ica/book/
	MRF Minimization Evaluation	MRF Optimization	Code	http://vision.middlebury.edu/MRF/	R. Szeliski et al., A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, PAMI, 2008
	Multi-View Stereo Evaluation	Multi-View Stereo	Code	http://vision.middlebury.edu/mview/	S. Seitz et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006
	Optical Flow Evaluation	Optical Flow	Code	http://vision.middlebury.edu/flow/	S. Baker et al. A Database and Evaluation Methodology for Optical Flow, IJCV, 2011
	Stereo Evaluation	Stereo	Code	http://vision.middlebury.edu/stereo/	D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV 2001
	Planar Graph Cut	MRF Optimization	Code	http://vision.csd.uwo.ca/code/PlanarCut-v1.0.zip	F. R. Schmidt, E. Toppe and D. Cremers, Efficient Planar Graph Cuts with Applications in Computer Vision, CVPR 2009
	Max-flow/min-cut	MRF Optimization	Code	http://vision.csd.uwo.ca/code/maxflow-v3.01.zip	Y. Boykov and V. Kolmogorov, An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, PAMI 2004
	Multi-label optimization	MRF Optimization	Code	http://vision.csd.uwo.ca/code/gco-v3.0.zip	Y. Boykov, O. Verksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001
	Max-flow/min-cut for massive grids	MRF Optimization	Code	http://vision.csd.uwo.ca/code/regionpushrelabel-v1.03.zip	A. Delong and Y. Boykov, A Scalable Graph-Cut Algorithm for N-D Grids, CVPR 2008
	Max-flow/min-cut for shape fitting	MRF Optimization	Code	http://www.csd.uwo.ca/faculty/yuri/Implementations/TouchExpand.zip	V. Lempitsky and Y. Boykov, Global Optimization for Shape Fitting, CVPR 2007
	Itti, Koch, and Niebur' saliency detection	Saliency Detection	Code	http://www.saliencytoolbox.net/	L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 1998
	Frequency-tuned salient region detection	Saliency Detection	Code	http://ivrgwww.epfl.ch/supplementary_material/RK_CVPR09/index.html	R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009
	Saliency detection using maximum symmetric surround	Saliency Detection	Code	http://ivrg.epfl.ch/supplementary_material/RK_ICIP2010/index.html	R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 2010
	Attention via Information Maximization	Saliency Detection	Code	http://www.cse.yorku.ca/~neil/AIM.zip	N. Bruce and J. Tsotsos. Saliency based on information maximization. In NIPS, 2005
	Context-aware saliency detection	Saliency Detection	Code	http://webee.technion.ac.il/labs/cgm/Computer-Graphics-Multimedia/Software/Saliency/Saliency.html	S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, 2010.
	Graph-based visual saliency	Saliency Detection	Code	http://www.klab.caltech.edu/~harel/share/gbvs.php	J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007
	Saliency detection: A spectral residual approach	Saliency Detection	Code	http://www.klab.caltech.edu/~xhou/projects/spectralResidual/spectralresidual.html	X. Hou and L. Zhang. Saliency detection: A spectral residual approach. CVPR, 2007
	Segmenting salient objects from images and videos	Saliency Detection	Code	http://www.cse.oulu.fi/MVG/Downloads/saliency	E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videos. CVPR, 2010
	Saliency Using Natural statistics	Saliency Detection	Code	http://cseweb.ucsd.edu/~l6zhang/	L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 2008
	Discriminant Saliency for Visual Recognition from Cluttered Scenes	Saliency Detection	Code	http://www.svcl.ucsd.edu/projects/saliency/	D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes, NIPS, 2004
	Learning to Predict Where Humans Look	Saliency Detection	Code	http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html	T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look, ICCV, 2009
	Global Contrast based Salient Region Detection	Saliency Detection	Code	http://cg.cs.tsinghua.edu.cn/people/~cmm/saliency/	M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection. CVPR, 2011
	Spatial Pyramid Matching	Image Classification	Code	http://www.cs.unc.edu/~lazebnik/research/SpatialPyramid.zip	S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, CVPR 2006
	Locality-constrained Linear Coding	Image Classification	Code	http://www.ifp.illinois.edu/~jyang29/LLC.htm	J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image Classification, CVPR, 2010
	Sparse Coding for Image Classification	Image Classification	Code	http://www.ifp.illinois.edu/~jyang29/ScSPM.htm	J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image Classification, CVPR, 2009
	Texture Classification	Image Classification	Code	http://www.robots.ox.ac.uk/~vgg/research/texclass/index.html	M. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005
	Multiple Kernels	Object Detection	Code	http://www.robots.ox.ac.uk/~vgg/software/MKL/	A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object Detection. ICCV, 2009
	Feature Combination	Object Detection	Code	http://www.vision.ee.ethz.ch/~pgehler/projects/iccv09/index.html	P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009
	SuperParsing	Image Understanding	Code	http://www.cs.unc.edu/~jtighe/Papers/ECCV10/eccv10-jtighe-code.zip	J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image Parsing with Superpixels, ECCV 2010
	Objectness measure	Object Proposal	Code	http://www.vision.ee.ethz.ch/~calvin/objectness/objectness-release-v1.01.tar.gz	B. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010
	Parametric min-cut	Object Proposal	Code	http://sminchisescu.ins.uni-bonn.de/code/cpmc/	J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010
	Region-based Object Proposal	Object Proposal	Code	http://vision.cs.uiuc.edu/proposals/	I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010
	Shadow Detection using Paired Region	Illumination, Reflectance, and Shadow	Code	http://www.cs.illinois.edu/homes/guo29/projects/shadow.html	R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011
	Ground shadow detection	Illumination, Reflectance, and Shadow	Code	http://www.jflalonde.org/software.html#shadowDetection	J.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs, ECCV 2010
	Dense Point Tracking	Optical Flow	Code	http://lmb.informatik.uni-freiburg.de/resources/binaries/	N. Sundaram, T. Brox, K. Keutzer Dense point trajectories by GPU-accelerated large displacement optical flow, ECCV 2010
	Large Displacement Optical Flow	Optical Flow	Code	http://lmb.informatik.uni-freiburg.de/resources/binaries/	T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, PAMI 2011
	Classical Variational Optical Flow	Optical Flow	Code	http://lmb.informatik.uni-freiburg.de/resources/binaries/	T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, ECCV 2004
	Nonlocal means with cluster trees	Image Denoising	Code	http://lmb.informatik.uni-freiburg.de/resources/binaries/nlmeans_brox_tip08Linux64.zip	T. Brox, O. Kleinschmidt, D. Cremers, Efficient nonlocal means for denoising of textural patterns, TIP 2008
	Sparse to Dense Labeling	Object Segmentation	Code	http://lmb.informatik.uni-freiburg.de/resources/binaries/SparseToDenseLabeling.tar.gz	P. Ochs, T. Brox, Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions, ICCV 2011
	Incremental Learning for Robust Visual Tracking	Visual Tracking	Code	http://www.cs.toronto.edu/~dross/ivt/	D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual Tracking, IJCV 2007
	MRF for image super-resolution	Image Super-resolution	Code	http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.html	W. T Freeman and C. Liu. Markov Random Fields for Super-resolution and Texture Synthesis. In A. Blake, P. Kohli, and C. Rother, eds., Advances in Markov Random Fields for Vision and Image Processing, Chapter 10. MIT Press, 2011
	Multi-frame image super-resolution	Image Super-resolution	Code	http://www.robots.ox.ac.uk/~vgg/software/SR/index.html	Pickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis
	MDSP Resolution Enhancement Software	Image Super-resolution	Code	http://users.soe.ucsc.edu/~milanfar/software/superresolution.html	S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Fast and Robust Multi-frame Super-resolution, TIP 2004
	Sprarse coding super-resolution	Image Super-resolution	Code	http://www.ifp.illinois.edu/~jyang29/ScSR.htm	J. Yang, J. Wright, T. S. Huang, and Y. Ma. Image super-resolution via sparse representation, TIP 2010
	Self-Similarities for Single Frame Super-Resolution	Image Super-resolution	Code	https://eng.ucmerced.edu/people/cyang35/ACCV10.zip	C.-Y. Yang, J.-B. Huang, and M.-H. Yang, Exploiting Self-Similarities for Single Frame Super-Resolution, ACCV 2010
	Eficient Marginal Likelihood Optimization in Blind Deconvolution	Image Deblurring	Code	http://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR2011Code.zip	A. Levin, Y. Weiss, F. Durand, W. T. Freeman. Efficient Marginal Likelihood Optimization in Blind Deconvolution, CVPR 2011
	Analyzing spatially varying blur	Image Deblurring	Code	http://www.eecs.harvard.edu/~ayanc/svblur/	A. Chakrabarti, T. Zickler, and W. T. Freeman, Analyzing Spatially-varying Blur, CVPR 2010
	Radon Transform	Image Deblurring	Code	http://people.csail.mit.edu/taegsang/Documents/RadonDeblurringCode.zip	T. S. Cho, S. Paris, B. K. P. Horn, W. T. Freeman, Blur kernel estimation using the radon transform, CVPR 2011
	Feature SIMilarity Index	Image Quality Assessment	Code	http://www4.comp.polyu.edu.hk/~cslzhang/IQA/FSIM/FSIM.htm
	Degradation Model	Image Quality Assessment	Code	http://users.ece.utexas.edu/~bevans/papers/2000/imageQuality/index.html
	Structural SIMilarity	Image Quality Assessment	Code	https://ece.uwaterloo.ca/~z70wang/research/ssim/
	SPIQA	Image Quality Assessment	Code	http://vision.ai.uiuc.edu/~bghanem2/shared_code/SPIQA_code.zip
	Kernel Density Estimation Toolbox	Density Estimation	Code	http://www.ics.uci.edu/~ihler/code/kde.html
	Dimensionality Reduction Toolbox	Dimension Reduction	Code	http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html
	ISOMAP	Dimension Reduction	Code	http://isomap.stanford.edu/
	LLE	Dimension Reduction	Code	http://www.cs.nyu.edu/~roweis/lle/code.html
	Laplacian Eigenmaps	Dimension Reduction	Code	http://www.cse.ohio-state.edu/~mbelkin/algorithms/Laplacian.tar
	Diffusion maps	Dimension Reduction	Code	http://www.stat.cmu.edu/~annlee/software.htm
	ANN: Approximate Nearest Neighbor Searching	Nearest Neighbors Matching	Code	http://www.cs.umd.edu/~mount/ANN/
	FLANN: Fast Library for Approximate Nearest Neighbors	Nearest Neighbors Matching	Code	http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN
	Bundler	Structure from motion	Code	http://phototour.cs.washington.edu/bundler/	N. Snavely, S M. Seitz, R Szeliski. Photo Tourism: Exploring image collections in 3D. SIGGRAPH 2006
	FIT3D	Structure from motion	Code	http://www.fit3d.info/
	Structure from Motion toolbox for Matlab by Vincent Rabaud	Structure from motion	Code	http://code.google.com/p/vincents-structure-from-motion-matlab-toolbox/
	Structure and Motion Toolkit in Matlab	Structure from motion	Code	http://cms.brookes.ac.uk/staff/PhilipTorr/Code/code_page_4.htm
	Nonrigid Structure From Motion in Trajectory Space	Structure from motion	Code	http://cvlab.lums.edu.pk/nrsfm/index.html
	libmv	Structure from motion	Code	http://code.google.com/p/libmv/
	VisualSFM : A Visual Structure from Motion System	Structure from motion	Code	http://www.cs.washington.edu/homes/ccwu/vsfm/
	Distance Transforms of Sampled Functions	Distance Transformation	Code	http://people.cs.uchicago.edu/~pff/dt/
	Fast Directional Chamfer Matching	Kernels and Distances	Code	http://www.umiacs.umd.edu/~mingyliu/src/fdcm_matlab_wrapper_v0.2.zip
	K-Means - VLFeat	Clustering	Code	http://www.vlfeat.org/
	K-Means - Oxford Code	Clustering	Code	http://www.cs.ucf.edu/~vision/Code/vggkmeans.zip
	Spectral Clustering - UW Project	Clustering	Code	http://www.stat.washington.edu/spectral/
	Spectral Clustering - UCSD Project	Clustering	Code	http://vision.ucsd.edu/~sagarwal/spectral-0.2.tgz
	Self-Tuning Spectral Clustering	Clustering	Code	http://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.html
	SHOGUN	Multiple Kernel Learning	Code	http://www.shogun-toolbox.org/	S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning. JMLR, 2006
	OpenKernel.org	Multiple Kernel Learning	Code	http://www.openkernel.org/	F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011
	DOGMA	Multiple Kernel Learning	Code	http://dogma.sourceforge.net/	F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010
	SimpleMKL	Multiple Kernel Learning	Code	http://asi.insa-rouen.fr/enseignants/~arakotom/code/mklindex.html	A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl. JMRL, 2008
	MIForests	Multiple Instance Learning	Code	http://www.ymer.org/amir/software/milforests/	C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010
	MILIS	Multiple Instance Learning	Code		Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010
	MILES	Multiple Instance Learning	Code	http://infolab.stanford.edu/~wangz/project/imsearch/SVM/PAMI06/	Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection. PAMI 2006
	DD-SVM	Multiple Instance Learning	Code		Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 2004
	GradientShop	Image Filtering	Code	http://grail.cs.washington.edu/projects/gradientshop/	P. Bhat, C.L. Zitnick, M. Cohen, B. Curless, and J. Kim, GradientShop: A Gradient-Domain Optimization Framework for Image and Video Filtering, TOG 2010
	Biologically motivated object recognition	Object Recognition	Code	http://cbcl.mit.edu/software-datasets/standardmodel/index.html	T. Serre, L. Wolf and T. Poggio. Object recognition with features inspired by visual cortex, CVPR 2005
	Ensemble of Exemplar-SVMs for Object Detection and Beyond	Object Detection	Code	http://www.cs.cmu.edu/~tmalisie/projects/iccv11/	T. Malisiewicz, A. Gupta, A. A. Efros, Ensemble of Exemplar-SVMs for Object Detection and Beyond , ICCV 2011
	Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics	Image Understanding	Code	http://www.cs.cmu.edu/~abhinavg/blocksworld/#downloads	A. Gupta, A. A. Efros, M. Hebert, Blocks World Revisited: Image Understanding using Qualitative Geometry and Mechanics, ECCV 2010
	Segmenting Scenes by Matching Image Composites	Image Segmentation	Code	http://www.cs.washington.edu/homes/bcr/projects/SceneComposites/index.html	B. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, NIPS 2009
	Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse Sequences	Illumination, Reflectance, and Shadow	Code	http://www.cs.cmu.edu/~jlalonde/software.html#skyModel	J-F. Lalonde, A. A. Efros, S. G. Narasimhan, Webcam Clip Art: Appearance and Illuminant Transfer from Time-lapse Sequences, SIGGRAPH Asia 2009
	Estimating Natural Illumination from a Single Outdoor Image	Illumination, Reflectance, and Shadow	Code	http://www.cs.cmu.edu/~jlalonde/software.html#skyModel	J-F. Lalonde, A. A. Efros, S. G. Narasimhan, Estimating Natural Illumination from a Single Outdoor Image , ICCV 2009
	What Does the Sky Tell Us About the Camera?	Illumination, Reflectance, and Shadow	Code	http://www.cs.cmu.edu/~jlalonde/software.html#skyModel	J-F. Lalonde, S. G. Narasimhan, A. A. Efros, What Does the Sky Tell Us About the Camera?, ECCV 2008
	Recognition by Association via Learning Per-exemplar Distances	Object Recognition	Code	http://www.cs.cmu.edu/~tmalisie/projects/cvpr08/dfuns.tar.gz	T. Malisiewicz, A. A. Efros, Recognition by Association via Learning Per-exemplar Distances, CVPR 2008
	Recovering Occlusion Boundaries from a Single Image	Image Segmentation	Code	http://www.cs.cmu.edu/~dhoiem/software/	D. Hoiem, A. Stein, A. A. Efros, M. Hebert, Recovering Occlusion Boundaries from a Single Image, ICCV 2007.
	Using Multiple Segmentations to Discover Objects and their Extent in Image Collections	Object Discovery	Code	http://people.csail.mit.edu/brussell/research/proj/mult_seg_discovery/index.html	B. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, Using Multiple Segmentations to Discover Objects and their Extent in Image Collections, CVPR 2006
	Image Quilting for Texture Synthesis and Transfer	Texture Synthesis	Code	http://www.cs.cmu.edu/~efros/quilt_research_code.zip	A. A. Efros and W. T. Freeman, Image Quilting for Texture Synthesis and Transfer, SIGGRAPH 2001
	Matlab Toolkit for Distance Metric Learning	Distance Metric Learning	Code	http://www.cs.cmu.edu/~liuy/distlearn.htm
	Nonparametric Scene Parsing via Label Transfer	Image Understanding	Code	http://people.csail.mit.edu/celiu/LabelTransfer/index.html	C. Liu, J. Yuen, and Antonio Torralba, Nonparametric Scene Parsing via Label Transfer, PAMI 2011
	Geodesic Star Convexity for Interactive Image Segmentation	Object Segmentation	Code	http://www.robots.ox.ac.uk/~vgg/software/iseg/index.shtml	V. Gulshan, C. Rother, A. Criminisi, A. Blake and A. Zisserman. Geodesic star convexity for interactive image segmentation
	Discriminative Models for Multi-Class Object Layout	Image Understanding	Code	http://www.ics.uci.edu/~desaic/multiobject_context.zip	C. Desai, D. Ramanan, C. Fowlkes. "Discriminative Models for Multi-Class Object Layout, IJCV 2011
	Articulated Pose Estimation using Flexible Mixtures of Parts	Pose Estimation	Code	http://phoenix.ics.uci.edu/software/pose/	Y. Yang, D. Ramanan, Articulated Pose Estimation using Flexible Mixtures of Parts, CVPR 2011
	Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects	Visual Tracking	Code	http://www.ics.uci.edu/~hpirsiav/papers/tracking_cvpr11_release_v1.0.tar.gz	H. Pirsiavash, D. Ramanan, C. Fowlkes. "Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects, CVPR 2011
	Training Deformable Models for Localization	Pose Estimation	Code	http://www.ics.uci.edu/~dramanan/papers/parse/index.html	Ramanan, D. "Learning to Parse Images of Articulated Bodies." NIPS 2006
	Low-Rank Matrix Recovery and Completion	Low-Rank Modeling	Code	http://perception.csl.uiuc.edu/matrix-rank/sample_code.html
	Spectral Hashing	Nearest Neighbors Matching	Code	http://www.cs.huji.ac.il/~yweiss/SpectralHashing/	Y. Weiss, A. Torralba, R. Fergus, Spectral Hashing, NIPS 2008
	What makes a good model of natural images ?	Image Denoising	Code	http://www.cs.huji.ac.il/~yweiss/BRFOE.zip	Y. Weiss and W. T. Freeman, CVPR 2007
	Generalized Principal Component Analysis	Subspace Learning	Code	http://www.vision.jhu.edu/downloads/main.php?dlID=c1	R. Vidal, Y. Ma and S. Sastry. Generalized Principal Component Analysis (GPCA), CVPR 2003
	Online Discriminative Object Tracking with Local Sparse Representation	Visual Tracking	Code	http://faculty.ucmerced.edu/mhyang/code/wacv12a_code.zip	Q. Wang, F. Chen, W. Xu, and M.-H. Yang, Online Discriminative Object Tracking with Local Sparse Representation, WACV 2012
	Superpixel Tracking	Visual Tracking	Code	http://faculty.ucmerced.edu/mhyang/papers/iccv11a.html	S. Wang, H. Lu, F. Yang, and M.-H. Yang, Superpixel Tracking, ICCV 2011
	Learning Hierarchical Image Representation with Sparsity, Saliency and Locality	Saliency Detection	Code		J. Yang and M.-H. Yang, Learning Hierarchical Image Representation with Sparsity, Saliency and Locality, BMVC 2011
	Estimating Human Pose from Occluded Images	Pose Estimation	Code	http://faculty.ucmerced.edu/mhyang/code/accv09_pose.zip	J.-B. Huang and M.-H. Yang, Estimating Human Pose from Occluded Images, ACCV 2009
	Visual Tracking with Histograms and Articulating Blocks	Visual Tracking	Code	http://www.cise.ufl.edu/~smshahed/tracking.htm	S. M. Shshed Nejhum, J. Ho, and M.-H.Yang, Visual Tracking with Histograms and Articulating Blocks, CVPR 2008
	Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing	Sparse Representation	Code	http://www.cs.technion.ac.il/~elad/Various/Matlab-Package-Book.rar	M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
	Single-Image Super-Resolution Matlab Package	Image Super-resolution	Code	http://www.cs.technion.ac.il/~elad/Various/Single_Image_SR.zip	R. Zeyde, M. Elad, and M. Protter, On Single Image Scale-Up using Sparse-Representations, LNCS 2010
	Robust Sparse Coding for Face Recognition	Sparse Representation	Code	http://www4.comp.polyu.edu.hk/~cslzhang/code/RSC.zip	M. Yang, L. Zhang, J. Yang and D. Zhang, “Robust Sparse Coding for Face Recognition,” CVPR 2011
	Centralized Sparse Representation for Image Restoration	Sparse Representation	Code	http://www4.comp.polyu.edu.hk/~cslzhang/code/CSR_IR.zip	W. Dong, L. Zhang and G. Shi, “Centralized Sparse Representation for Image Restoration,” ICCV 2011
	A Linear Subspace Learning Approach via Sparse Coding	Sparse Representation	Code	http://www4.comp.polyu.edu.hk/~cslzhang/code/LSL_SC.zip	L. Zhang, P. Zhu, Q. Hu and D. Zhang, “A Linear Subspace Learning Approach via Sparse Coding,” ICCV 2011
	Fisher Discrimination Dictionary Learning for Sparse Representation	Sparse Representation	Code	http://www4.comp.polyu.edu.hk/~cslzhang/code/FDDL.zip	M. Yang, L. Zhang, X. Feng and D. Zhang, Fisher Discrimination Dictionary Learning for Sparse Representation, ICCV 2011
	Hyper-graph Matching via Reweighted Random Walks	Graph Matching	Code	http://cv.snu.ac.kr/research/~RRWHM/	J. Lee, M. Cho, K. M. Lee. "Hyper-graph Matching via Reweighted Random Walks", CVPR 2011
	Reweighted Random Walks for Graph Matching	Graph Matching	Code	http://cv.snu.ac.kr/research/~RRWM/	M. Cho, J. Lee, and K. M. Lee, Reweighted Random Walks for Graph Matching, ECCV 2010
	Visual Tracking Decomposition	Visual Tracking	Code	http://cv.snu.ac.kr/research/~vtd/	J Kwon and K. M. Lee, Visual Tracking Decomposition, CVPR 2010
	SPArse Modeling Software	Sparse Representation	Code	http://www.di.ens.fr/willow/SPAMS/	J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online Learning for Matrix Factorization and Sparse Coding, JMLR 2010
	OpenSourcePhotogrammetry	Structure from motion	Code	http://opensourcephotogrammetry.blogspot.com/
	Clustering Views for Multi-view Stereo	Multi-View Stereo	Code	http://grail.cs.washington.edu/software/cmvs/	Y. Furukawa, B. Curless, S. M. Seitz, and R. Szeliski, Towards Internet-scale Multi-view Stereo, CVPR 2010
	Patch-based Multi-view Stereo Software	Multi-View Stereo	Code	http://grail.cs.washington.edu/software/pmvs/	Y. Furukawa and J. Ponce, Accurate, Dense, and Robust Multi-View Stereopsis, PAMI 2009
	Towards Total Scene Understanding	Image Understanding	Code	http://vision.stanford.edu/projects/totalscene/index.html	L.-J. Li, R. Socher and Li F.-F.. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework, CVPR 2009
	Object Bank	Image Understanding	Code	http://vision.stanford.edu/projects/objectbank/index.html	Li-Jia Li, Hao Su, Eric P. Xing and Li Fei-Fei. Object Bank: A High-Level Image Representation for Scene Classification and Semantic Feature Sparsification, NIPS 2010
	Coherency Sensitive Hashing	Nearest Neighbors Matching	Code	http://www.eng.tau.ac.il/~simonk/CSH/index.html	S. Korman, S. Avidan, Coherency Sensitive Hashing, ICCV 2011
	RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition	Low-Rank Modeling	Code	http://perception.csl.uiuc.edu/matrix-rank/rasl.html	Y. Peng, A. Ganesh, J. Wright, W. Xu, and Y. Ma, RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition, CVPR 2010
	TILT: Transform Invariant Low-rank Textures	Low-Rank Modeling	Code	http://perception.csl.uiuc.edu/matrix-rank/tilt.html	Z. Zhang, A. Ganesh, X. Liang, and Y. Ma, TILT: Transform Invariant Low-rank Textures, IJCV 2011
	BRIEF: Binary Robust Independent Elementary Features	Feature Extraction	Code	http://cvlab.epfl.ch/research/detect/brief/	M. Calonder, V. Lepetit, C. Strecha, P. Fua, BRIEF: Binary Robust Independent Elementary Features, ECCV 2010
	Sketching the Common	Common Visual Pattern Discovery	Code	http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/SketchCommonCVPR10_v1.1.tar.gz	S. Bagon, O. Brostovsky, M. Galun and M. Irani, Detecting and Sketching the Common, CVPR 2010
	Common Visual Pattern Discovery via Spatially Coherent Correspondences	Common Visual Pattern Discovery	Code	https://sites.google.com/site/lhrbss/home/papers/SimplifiedCode.zip?attredirects=0	H. Liu, S. Yan, "Common Visual Pattern Discovery via Spatially Coherent Correspondences", CVPR 2010
	Richardson-Lucy Deblurring for Scenes under Projective Motion Path	Image Deblurring	Code	http://yuwing.kaist.ac.kr/projects/projectivedeblur/projectivedeblur_files/ProjectiveDeblur.zip	Y.-W. Tai, P. Tan, M. S. Brown: Richardson-Lucy Deblurring for Scenes under Projective Motion Path, PAMI 2011
	sRD-SIFT	Feature Extraction	Code	http://arthronav.isr.uc.pt/~mlourenco/srdsift/index.html#	M. Lourenco, J. P. Barreto and A. Malti, Feature Detection and Matching in Images with Radial Distortion, ICRA 2010
	Constant-Space Belief Propagation	Stereo	Code	http://www.cs.cityu.edu.hk/~qiyang/publications/code/cvpr-10-csbp/csbp.htm	Q. Yang, L. Wang, and N. Ahuja, A Constant-Space Belief Propagation Algorithm for Stereo Matching, CVPR 2010
	Real-time Specular Highlight Removal	Illumination, Reflectance, and Shadow	Code	http://www.cs.cityu.edu.hk/~qiyang/publications/code/eccv-10.zip	Q. Yang, S. Wang and N. Ahuja, Real-time Specular Highlight Removal Using Bilateral Filtering, ECCV 2010
	Saliency-based video segmentation	Saliency Detection	Code	http://www.brl.ntt.co.jp/people/akisato/saliency3.html	K. Fukuchi, K. Miyazato, A. Kimura, S. Takagi and J. Yamato, Saliency-based video segmentation with graph cuts and sequentially updated priors, ICME 2009
	Neocognitron for handwritten digit recognition	Text Recognition	Code	http://visiome.neuroinf.jp/modules/xoonips/detail.php?item_id=375	K. Fukushima: "Neocognitron for handwritten digit recognition", Neurocomputing, 2003
	Non-blind deblurring (and blind denoising) with integrated noise estimation	Image Deblurring	Code	http://www.gris.tu-darmstadt.de/research/visinf/software/index.en.htm	U. Schmidt, K. Schelten, and S. Roth. Bayesian deblurring with integrated noise estimation, CVPR 2011
	LDAHash: Binary Descriptors for Matching in Large Image Databases	Nearest Neighbors Matching	Code	http://cvlab.epfl.ch/research/detect/ldahash/index.php	C. Strecha, A. M. Bronstein, M. M. Bronstein and P. Fua. LDAHash: Improved matching with smaller descriptors, PAMI, 2011.
	Sparse coding simulation software	Sparse Representation	Code	http://redwood.berkeley.edu/bruno/sparsenet/	Olshausen BA, Field DJ, "Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images", Nature 1996
	Efficient sparse coding algorithms	Sparse Representation	Code	http://ai.stanford.edu/~hllee/softwares/nips06-sparsecoding.htm	H. Lee, A. Battle, R. Rajat and A. Y. Ng, Efficient sparse coding algorithms, NIPS 2007
	Spectrum Scale Space based Visual Saliency	Saliency Detection	Code	http://www.cim.mcgill.ca/~lijian/saliency.htm	J Li, M D. Levine, X An and H. He, Saliency Detection Based on Frequency and Spatial Domain Analyses, BMVC 2011
	Tracking using Pixel-Wise Posteriors	Visual Tracking	Code	http://www.robots.ox.ac.uk/~cbibby/research_pwp.shtml	C. Bibby and I. Reid, Tracking using Pixel-Wise Posteriors, ECCV 2008
	Game Theory in Computer Vision and Pattern Recognition	Game Theory	Tutorial	http://www.dsi.unive.it/~atorsell/cvpr2011tutorial/	Marcello Pelillo and Andrea Torsello, CVPR 2011 Tutorial
	Image and Video Description with Local Binary Pattern Variants	Feature Extraction	Tutorial	http://www.ee.oulu.fi/research/imag/mvg/files/pdf/CVPR-tutorial-final.pdf	M. Pietikainen and J. Heikkila, CVPR 2011 Tutorial
	Tools and Methods for Image Registration	Image Registration	Tutorial	http://www.imgfsr.com/CVPR2011/Tutorial6/	Brown, G. Carneiro, A. A. Farag, E. Hancock, A. A. Goshtasby (Organizer), J. Matas, J.M. Morel, N. S. Netanyahu, F. Sur, and G. Yu, CVPR 2011 Tutorial
	Frontiers of Human Activity Analysis	Action Recognition	Tutorial	http://cvrc.ece.utexas.edu/mryoo/cvpr2011tutorial/	J. K. Aggarwal, Michael S. Ryoo, and Kris Kitani, CVPR 2011 Tutorial
	Diffusion Geometry Methods in Shape Analysis	Shape Analysis, Diffusion Geometry	Tutorial	http://tosca.cs.technion.ac.il/book/course_eccv10.html	A. Brontein and M. Bronstein, ECCV 2010 Tutorial
	Structured Prediction and Learning in Computer Vision	Structured Prediction	Tutorial	http://www.nowozin.net/sebastian/cvpr2011tutorial/	S. Nowozin and C. Lampert, CVPR 2011 Tutorial
	Variational Methods in Computer Vision	Variational Calculus	Tutorial	http://cvpr.cs.tum.edu/tutorials/eccv2010	D. Cremers, B. Goldlücke, T. Pock, ECCV 2010 Tutorial
	Computer Vision and 3D Perception for Robotics	3D perception	Tutorial	http://www.willowgarage.com/workshops/2010/eccv	Radu Bogdan Rusu, Gary Bradski, Caroline Pantofaru, Stefan Hinterstoisser, Stefan Holzer, Kurt Konolige and Andrea Vedaldi, ECCV 2010 Tutorial
	Computational Symmetry: Past, Current, Future	Computational Symmetry	Tutorial	http://vision.cse.psu.edu/research/symmComp/index.shtml	Yanxi Liu, ECCV 2010 Tutorial
	Feature Learning for Image Classification	Feature Learning, Image Classification	Tutorial	http://ufldl.stanford.edu/eccv10-tutorial/	Kai Yu and Andrew Ng, ECCV 2010 Tutorial
	Statistical and Structural Recognition of Human Actions	Action Recognition	Tutorial	https://sites.google.com/site/humanactionstutorialeccv10/	Ivan Laptev and Greg Mori, ECCV 2010 Tutorial
	Distance Functions and Metric Learning	Distance Metric Learning	Tutorial	http://www.cs.huji.ac.il/~ofirpele/DFML_ECCV2010_tutorial/	M. Werman, O. Pele and B. Kulis, ECCV 2010 Tutorial
	Nonrigid Structure from Motion	Structure from motion	Tutorial	http://www.cs.cmu.edu/~yaser/ECCV2010Tutorial.html	Y. Sheikh and Sohaib Khan, ECCV 2010 Tutorial
	Looking at people: The past, the present and the future	Action Recognition	Tutorial	http://www.cs.brown.edu/~ls/iccv2011tutorial.html	L. Sigal, T. Moeslund, A. Hilton, V. Kruger, ICCV 2011 Tutorial
	3D point cloud processing: PCL (Point Cloud Library)	3D point cloud processing	Tutorial	http://www.pointclouds.org/media/iccv2011.html	R. Rusu, S. Holzer, M. Dixon, V. Rabaud, ICCV 2011 Tutorial
	Fcam: an architecture and API for computational cameras	Computational Imaging	Tutorial	http://fcam.garage.maemo.org/iccv2011.html	Kari Pulli, Andrew Adams, Timo Ahonen, Marius Tico, ICCV 2011 Tutorial
	Variational methods for computer vision	Variational Calculus	Tutorial	http://cvpr.in.tum.de/tutorials/iccv2011	Daniel Cremers, Bastian Goldlucke, Thomas Pock, ICCV 2011 Tutorial
	Non-rigid registration and reconstruction	Non-rigid registration	Tutorial	http://www.isr.ist.utl.pt/~adb/tutorial/	Alessio Del Bue, Lourdes Agapito, Adrien Bartoli, ICCV 2011 Tutorial
	Learning with inference for discrete graphical models	Graphical Models	Tutorial	http://www.csd.uoc.gr/~komod/ICCV2011_tutorial/	Nikos Komodakis, Pawan Kumar, Nikos Paragios, Ramin Zabih, ICCV 2011 Tutorial
	Computer vision fundamentals: robust non-linear least-squares and their applications	Non-linear Least Squares	Tutorial	http://cvlab.epfl.ch/~fua/courses/lsq/	Pascal Fua, Vincent Lepetit, ICCV 2011 Tutorial
	Geometry constrained parts based detection	Object Detection	Tutorial	http://ci2cv.net/tutorials/iccv-2011/	Simon Lucey, Jason Saragih, ICCV 2011 Tutorial
	Decision forests for classification, regression, clustering and density estimation	Decision Forests	Tutorial	http://research.microsoft.com/en-us/groups/vision/decisionforests.aspx	A. Criminisi, J. Shotton and E. Konukoglu, ICCV 2011 Tutorial
	Color image understanding: from acquisition to high-level image understanding	Color Image Processing	Tutorial	http://www.cat.uab.cat/~joost/tutorial_iccv.html	Theo Gevers, Keigo Hirakawa, Joost van de Weijer, ICCV 2011 Tutorial
	Advances in Computer Vision	Computer Vision	Course	http://groups.csail.mit.edu/vision/courses/6.869/	Antonio Torralba, MIT, Spring 2010
	Introduction to Computer Vision	Computer Vision	Course	http://www.cs.brown.edu/courses/cs143/	James Hays, Brown University, Fall 2011
	The Computer Vision Industry	Computer Vision Industry	Link	http://www.cs.ubc.ca/~lowe/vision.html	David Lowe
	Computer Vision, New York University, Fall 2012	Computer Vision	Course	http://cs.nyu.edu/~fergus/teaching/vision_2012/index.html	Rob Fergus
	Visual Recognition, University of Texas at Austin, Fall 2011	Visual Recognition	Course	http://www.cs.utexas.edu/~grauman/courses/fall2011/schedule.html	Kristen Grauman
	Computer Vision, University of Texas at Austin, Spring 2011	Computer Vision	Course	http://www.cs.utexas.edu/~grauman/courses/spring2011/index.html	Kristen Grauman
	Learning-Based Methods in Vision, CMU, Spring 2012	Computer Vision	Course	https://docs.google.com/document/pub?id=1jGBn7zPDEaU33fJwi3YI_usWS-U6gpSSJotV_2gDrL0	Alexei “Alyosha” Efros and Leonid Sigal
	Computational Photography, CMU, Fall 2011	Computational Photography	Course	http://graphics.cs.cmu.edu/courses/15-463/2011_fall/463.html	Alexei “Alyosha” Efros
	Computer Vision, University of North Carolina at Chapel Hill, Spring 2010	Computer Vision	Course	http://www.cs.unc.edu/~lazebnik/spring10/	Svetlana Lazebnik
	Matlab Tutorial	Matlab	Tutorial	http://www.cs.unc.edu/~lazebnik/spring10/matlab.intro.html	David Kriegman and Serge Belongie
	Computer Vision: The Fundamentals, University of California at Berkeley, Fall 2012	Computer Vision	Course	https://www.coursera.org/course/vision	Jitendra Malik
	Computer Vision: From 3D Reconstruction to Visual Recognition, Fall 2012	Computer Vision	Course	https://www.coursera.org/course/computervision	Silvio Savarese and Fei-Fei Li
	Introduction to Computer Vision, Stanford University, Winter 2010-2011	Computer Vision	Course	http://vision.stanford.edu/teaching/cs223b/	Fei-Fei Li
	Computer Vision, University of Washington, Winter 2012	Computer Vision	Course	http://www.cs.washington.edu/education/courses/cse455/12wi/	Steven Seitz
	The Computer Vision homepage	Computer Vision	Link	http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html
	CVonline	Computer Vision	Link	http://homepages.inf.ed.ac.uk/rbf/CVonline/	CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision
	Annotated Computer Vision Bibliography	Computer Vision	Link	http://iris.usc.edu/Vision-Notes/bibliography/contents.html	compiled by Keith Price
	Computer Vision, University of Illinois, Urbana-Champaign, Spring 2012	Computer Vision	Course	http://www.cs.illinois.edu/class/sp12/cs543/	Derek Hoiem
	Computational Photography, University of Illinois, Urbana-Champaign, Fall 2011	Computational Photography	Course	http://www.cs.illinois.edu/class/fa11/cs498dh/	Derek Hoiem
	3D Computer Vision: Past, Present, and Future	3D Computer Vision	Talk	http://www.youtube.com/watch?v=kyIzMr917Rc	Steven Seitz, University of Washington, Google Tech Talk, 2011
	Theory and Applications of Boosting	Boosting	Talk	http://videolectures.net/mlss09us_schapire_tab/	Robert Schapire, Department of Computer Science, Princeton University
	Learning in Hierarchical Architectures: from Neuroscience to Derived Kernels	Neuroscience	Talk	http://videolectures.net/mlss09us_poggio_lhandk/	Tomaso A. Poggio, McGovern Institute for Brain Research, Massachusetts Institute of Technology
	Optimization Algorithms in Support Vector Machines	Optimization and Support Vector Machines	Talk	http://videolectures.net/mlss09us_wright_oasvm/	Stephen J. Wright, Computer Sciences Department, University of Wisconsin - Madison
	Convex Optimization	Optimization	Talk	http://videolectures.net/mlss2011_vandenberghe_convex/	Lieven Vandenberghe, Electrical Engineering Department, University of California, Los Angeles
	Optimization Algorithms in Machine Learning	Optimization	Talk	http://videolectures.net/nips2010_wright_oaml/	Stephen J. Wright, Computer Sciences Department, University of Wisconsin - Madison
	Introduction To Bayesian Inference	Bayesian Inference	Talk	http://videolectures.net/mlss09uk_bishop_ibi/	Christopher Bishop, Microsoft Research
	Information Theory	Information Theory	Talk	http://videolectures.net/mlss09uk_mackay_it/	David MacKay, University of Cambridge
	Gaussian Process Basics	Gaussian Process	Talk	http://videolectures.net/gpip06_mackay_gpb/	David MacKay, University of Cambridge
	Statistical Learning Theory	Statistical Learning Theory	Talk	http://videolectures.net/mlss04_taylor_slt/	John Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College London
	Learning and Inference in Low-Level Vision	Low-level vision	Talk	http://videolectures.net/nips09_weiss_lil/	Yair Weiss, School of Computer Science and Engineering, The Hebrew University of Jerusalem
	Energy Minimization with Label costs and Applications in Multi-Model Fitting	Optimization	Talk	http://videolectures.net/nipsworkshops2010_boykov_eml/	Yuri Boykov, Department of Computer Science, University of Western Ontario
	Graphical Models and message-passing algorithms	Graphical Models	Talk	http://videolectures.net/mlss2011_wainwright_messagepassing/	Martin J. Wainwright, University of California at Berkeley
	Who is Afraid of Non-Convex Loss Functions?	Optimization	Talk	http://videolectures.net/eml07_lecun_wia/	Yann LeCun, New York University
	A tutorial on Deep Learning	Deep Learning	Talk	http://videolectures.net/jul09_hinton_deeplearn/	Geoffrey E. Hinton, Department of Computer Science, University of Toronto
	Relative Entropy	Relative Entropy	Talk	http://videolectures.net/nips09_verdu_re/	Sergio Verdu, Princeton University
	Sparse Methods for Machine Learning: Theory and Algorithms	Sparse Representation	Talk	http://videolectures.net/nips09_bach_smm/	Francis R. Bach, INRIA
	Information Theory in Learning and Control	Information Theory	Talk	http://www.youtube.com/watch?v=GKm53xGbAOk&feature=relmfu	Naftali (Tali) Tishby, The Hebrew University
	Modern Bayesian Nonparametrics	Bayesian Nonparametrics	Talk	http://www.youtube.com/watch?v=F0_ih7THV94&feature=relmfu	Peter Orbanz and Yee Whye Teh
	Understanding Visual Scenes	Visual Recognition	Talk	http://videolectures.net/nips09_torralba_uvs/	Antonio Torralba, MIT
	Machine learning and kernel methods for computer vision	Kernels and Distances	Talk	http://videolectures.net/etvc08_bach_mlakm/	Francis R. Bach, INRIA
	Graphical Models, Exponential Families, and Variational Inference	Graphical Models	Tutorial	http://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf	Martin J. Wainwright and Michael I. Jordan, University of California at Berkeley
	A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models	Expectation Maximization	Tutorial	http://crow.ee.washington.edu/people/bulyko/papers/em.pdf	Jeff A. Bilmes, University of California at Berkeley
	A Tutorial on Spectral Clustering	Spectral Clustering	Tutorial	http://web.mit.edu/~wingated/www/introductions/tutorial_on_spectral_clustering.pdf	Ulrike von Luxburg, Max Planck Institute for Biological Cybernetics
	Object Recognition with Deformable Models	Object Detection	Talk	http://www.youtube.com/watch?v=_J_clwqQ4gI	Pedro Felzenszwalb, Brown University
	Inference in Graphical Models, Stanford University, Spring 2012	Graphical Models	Course	http://www.stanford.edu/~montanar/TEACHING/Stat375/stat375.html	Andrea Montanari, Stanford University
	Writing Fast MATLAB Code	Matlab	Tutorial	http://www.mathworks.com/matlabcentral/fileexchange/5685	Pascal Getreuer, Yale University
	Source Code Collection for Reproducible Research	Source code	Link	http://www.csee.wvu.edu/~xinl/reproducible_research.html	collected by Xin Li, Lane Dept of CSEE, West Virginia University
	Computer Vision Algorithm Implementations	Source code	Link	http://www.cvpapers.com/rr.html	CVPapers
	CV Papers on the web	Computer Vision	Link	http://www.cvpapers.com/index.html	CVPapers
	CV Datasets on the web	Computer Vision	Link	http://www.cvpapers.com/datasets.html	CVPapers
	Computer Image Analysis, Computer Vision Conferences	Computer Vision	Link	http://iris.usc.edu/information/Iris-Conferences.html	USC
	Compiled list of recognition datasets	Dataset	Link	http://www.cs.utexas.edu/~grauman/courses/spring2008/datasets.htm	compiled by Kristen Grauman
	Detecting faces in images: A survey	Face Detection	Survey	http://faculty.ucmerced.edu/mhyang/facedetection.html	MH Yang, DJ Kriegman, N Ahuja, PAMI 2002
	Face recognition: A literature survey	Face Recognition	Survey	http://www.cs.ucf.edu/~dcm/Teaching/COT4810-Spring2011/Literature/DiegoVelasquez-FaceRecognitionLiteratureSurvey.pdf	W. Zhao , R. Chellappa, P. J. Phillips, A. Rosenfeld, ACM Computing Surveys, 2003
	Data clustering: a review	Clustering	Survey	http://nd.edu/~flynn/papers/Jain-CSUR99.pdf	Anil K Jain, M Narasimha Murty, Patrick J Flynn, ACM Computing Surveys, 1999
	Statistical pattern recognition: A review	Statistical Pattern Recognition	Survey	http://homepage.tudelft.nl/a9p19/papers/pami_00_review.pdf	Anil K. Jain, Robert P. W. Duin, Jianchang Mao, PAMI 2000
	Face detection in color images	Face Detection	Survey	http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1000242&tag=1	Rein-Lien Hsu, Mohamed Abdel-Mottaleb, Anil K. Jain, PAMI 2002
	An Introduction to Biometric Recognition	Biometric Recognition	Survey	http://www.csee.wvu.edu/~ross/pubs/RossBioIntro_CSVT2004.pdf	Anil K. Jain, Arun Ross, Salil Prabhakar, CSVT 2004
	Image registration methods: a survey	Image Registration	Survey	http://library.utia.cas.cz/prace/20030125.pdf	Barbara Zitova, Jan Flusser, Image and Vision Computing, 2003
	Image alignment and stitching: a tutorial	Image Registration, Image Stitching	Tutorial	http://www.cs.washington.edu/education/courses/cse576/05sp/papers/MSR-TR-2004-92.pdf	Richard Szeliski, Foundations and Trends® in Computer Graphics and Vision, 2006
	A Survey of Computer Vision-Based Human Motion Capture	Motion Capture	Survey	http://cronos.rutgers.edu/~meer/TEACHTOO/PAPERS/moeslund06.pdf	Thomas B. Moeslund, Adrian Hilton, Volker Kruger, CVIU, 2006
	Detecting moving shadows: Algorithms and evaluation	Illumination, Reflectance, and Shadow	Survey	http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01206520	A Prati, I Mikic, MM Trivedi, R. Cucchiara, PAMI 2003
	Discrete-continuous optimization for large-scale structure from motion	Structure from motion	Code	http://vision.soic.indiana.edu/wp/wp-content/uploads/disco-bp.zip	David Crandall, Andrew Owens, Noah Snavely, Dan Huttenlocher, Discrete-Continuous Optimization for Large-scale Structure from Motion, CVPR 2011
	Learning hierarchical spatio-temporal features for action recognition with independent subspace analysis	Action Recognition	Code	http://ai.stanford.edu/~wzou/release.tar.gz	Q.V. Le, W.Y. Zou, S.Y. Yeung, A.Y. Ng. "Learning hierarchical spatio-temporal features for action recognition with independent subspace analysis", CVPR 2011
	Non-linear least squares solver	Optimization	Code	http://code.google.com/p/ceres-solver/	http://google-opensource.blogspot.com/2012/05/introducing-ceres-solver-nonlinear.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