UIUC同学Jia-Bin Huang收集的计算机视觉代码合集（ZZ）_ucizz

作者：Monodyee | 2024-05-20 02:04:26

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ucizz

转自：http://www.cnblogs.com/idaidai/archive/2012/03/01/2375800.html

UIUC的Jia-Bin Huang同学收集了很多计算机视觉方面的代码，链接如下：

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

248 item

Topic	Name	Reference	Link
Feature Detection,Feature Extraction, and Action 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
Action Recognition	3D Gradients (HOG3D)	A. Klaser, M. Marszałek, and C. Schmid, BMVC, 2008.	http://lear.inrialpes.fr/people/klaeser/research_hog3d
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
Alpha Matting	Spectral Matting	A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008	http://www.vision.huji.ac.il/SpectralMatting/
Alpha Matting	Shared Matting	E. S. L. Gastal and M. M. Oliveira, Computer Graphics Forum, 2010	http://www.inf.ufrgs.br/~eslgastal/SharedMatting/
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
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
Alpha Matting	Learning-based Matting	Y. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009	http://www.mathworks.com/matlabcentral/fileexchange/31412
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/
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/
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/
Clustering	Spectral Clustering - UW Project		http://www.stat.washington.edu/spectral/
Clustering	Spectral Clustering - UCSD Project		http://vision.ucsd.edu/~sagarwal/spectral-0.2.tgz
Clustering	Self-Tuning Spectral Clustering		http://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.html
Clustering	K-Means - Oxford Code		http://www.cs.ucf.edu/~vision/Code/vggkmeans.zip
Clustering	K-Means - VLFeat		http://www.vlfeat.org/
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
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
Density Estimation	Kernel Density Estimation Toolbox		http://www.ics.uci.edu/~ihler/code/kde.html
Depth Sensor	Kinect SDK	http://www.microsoft.com/en-us/kinectforwindows/	http://www.microsoft.com/en-us/kinectforwindows/
Dimension Reduction	ISOMAP		http://isomap.stanford.edu/
Dimension Reduction	LLE		http://www.cs.nyu.edu/~roweis/lle/code.html
Dimension Reduction	Laplacian Eigenmaps		http://www.cse.ohio-state.edu/~mbelkin/algorithms/Laplacian.tar
Dimension Reduction	Diffusion maps		http://www.stat.cmu.edu/~annlee/software.htm
Dimension Reduction	Dimensionality Reduction Toolbox		http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html
Distance Metric Learning	Matlab Toolkit for Distance Metric Learning		http://www.cs.cmu.edu/~liuy/distlearn.htm
Distance Transformation	Distance Transforms of Sampled Functions		http://people.cs.uchicago.edu/~pff/dt/
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
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
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
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
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/
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/
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/
Feature Extraction	Global and Efficient Self-Similarity	T. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and Detection. CVPR 2010and 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
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/
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/
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/
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/
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
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
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
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
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/
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/
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/
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/
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/
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
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/
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#
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/
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/
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
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
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
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
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
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
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
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
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
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
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
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
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/
Image Denoising,Image Super-resolution, and Image 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/
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
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
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
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
Image Denoising	K-SVD		http://www.cs.technion.ac.il/~ronrubin/Software/ksvdbox13.zip
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/
Image Denoising	BLS-GSM		http://decsai.ugr.es/~javier/denoise/
Image Denoising	Field of Experts		http://www.cs.brown.edu/~roth/research/software.html
Image Denoising	Non-local Means		http://dmi.uib.es/~abuades/codis/NLmeansfilter.m
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
Image Denoising	BM3D		http://www.cs.tut.fi/~foi/GCF-BM3D/
Image Denoising	Kernel Regressions		http://www.soe.ucsc.edu/~htakeda/MatlabApp/KernelRegressionBasedImageProcessingToolBox_ver1-1beta.zip
Image Denoising	Gaussian Field of Experts		http://www.cs.huji.ac.il/~yweiss/BRFOE.zip
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
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/
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/
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
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
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/
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
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
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
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
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
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
Image Quality Assessment	SPIQA		http://vision.ai.uiuc.edu/~bghanem2/shared_code/SPIQA_code.zip
Image Quality Assessment	Degradation Model		http://users.ece.utexas.edu/~bevans/papers/2000/imageQuality/index.html
Image Quality Assessment	Feature SIMilarity Index		http://www4.comp.polyu.edu.hk/~cslzhang/IQA/FSIM/FSIM.htm
Image Quality Assessment	Structural SIMilarity		https://ece.uwaterloo.ca/~z70wang/research/ssim/
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/
Image Segmentation	Normalized Cut	J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000	http://www.cis.upenn.edu/~jshi/software/
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
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
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
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/
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
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/
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/
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
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/
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
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
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
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
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
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
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
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
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
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
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
Image Understanding	SuperParsing	J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image Parsing with Superpixels, ECCV 2010	http://www.cs.unc.edu/~jtighe/Papers/ECCV10/eccv10-jtighe-code.zip
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
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
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
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
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
Kernels and Distances	Fast Directional Chamfer Matching		http://www.umiacs.umd.edu/~mingyliu/src/fdcm_matlab_wrapper_v0.2.zip
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
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
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
Low-Rank Modeling	Low-Rank Matrix Recovery and Completion		http://perception.csl.uiuc.edu/matrix-rank/sample_code.html
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
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/
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
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
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
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
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
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/
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/
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
Machine Learning	FastICA package for MATLAB	http://research.ics.tkk.fi/ica/book/	http://research.ics.tkk.fi/ica/fastica/
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/
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/
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/
Multiple Instance Learning	DD-SVM	Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with Regions, JMLR 2004
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/
Multiple Instance Learning	MILIS	Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010
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/
Multiple Kernel Learning	SHOGUN	S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning. JMLR, 2006	http://www.shogun-toolbox.org/
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/
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
Multiple Kernel Learning	DOGMA	F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010	http://dogma.sourceforge.net/
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
Multiple View Geometry	Matlab Functions for Multiple View Geometry		http://www.robots.ox.ac.uk/~vgg/hzbook/code/
Nearest Neighbors Matching	ANN: Approximate Nearest Neighbor Searching		http://www.cs.umd.edu/~mount/ANN/
Nearest Neighbors Matching	Spectral Hashing	Y. Weiss, A. Torralba, R. Fergus, Spectral Hashing, NIPS 2008	http://www.cs.huji.ac.il/~yweiss/SpectralHashing/
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
Nearest Neighbors Matching	FLANN: Fast Library for Approximate Nearest Neighbors		http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN
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
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/
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/
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/
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
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/
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
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
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
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
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
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/
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
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/
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
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/
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
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
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
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/
Object Proposal	Region-based Object Proposal	I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010	http://vision.cs.uiuc.edu/proposals/
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
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
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
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
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
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
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/
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/
Optical Flow	Dense Point Tracking	N. Sundaram, T. Brox, K. Keutzer Dense point trajectories by GPU-accelerated large displacement optical flow, ECCV 2010	http://lmb.informatik.uni-freiburg.de/resources/binaries/
Optical Flow	Optical Flow Evaluation	S. Baker et al. A Database and Evaluation Methodology for Optical Flow, IJCV, 2011	http://vision.middlebury.edu/flow/
Optical Flow	Horn and Schunck's Optical Flow		http://www.cs.brown.edu/~dqsun/code/hs.zip
Optical Flow	Black and Anandan's Optical Flow		http://www.cs.brown.edu/~dqsun/code/ba.zip
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
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/
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/
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
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
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/
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
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/
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
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
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
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
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
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
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/
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
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
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/
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
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
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
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
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
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
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/
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/
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
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
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/
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/
Structure from motion	Nonrigid Structure From Motion in Trajectory Space		http://cvlab.lums.edu.pk/nrsfm/index.html
Structure from motion	libmv		http://code.google.com/p/libmv/
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/
Structure from motion	FIT3D		http://www.fit3d.info/
Structure from motion	VisualSFM : A Visual Structure from Motion System		http://www.cs.washington.edu/homes/ccwu/vsfm/
Structure from motion	OpenSourcePhotogrammetry		http://opensourcephotogrammetry.blogspot.com/
Structure from motion	Structure and Motion Toolkit in Matlab		http://cms.brookes.ac.uk/staff/PhilipTorr/Code/code_page_4.htm
Structure from motion	Structure from Motion toolbox for Matlab by Vincent Rabaud		http://code.google.com/p/vincents-structure-from-motion-matlab-toolbox/
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
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/
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
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
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/
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
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
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/
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
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
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/
Visual Tracking	Online boosting trackers	H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR, 2006	http://www.vision.ee.ethz.ch/boostingTrackers/
Visual Tracking	Visual Tracking Decomposition	J Kwon and K. M. Lee, Visual Tracking Decomposition, CVPR 2010	http://cv.snu.ac.kr/research/~vtd/
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
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
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
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
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
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/
Visual Tracking	Particle Filter Object Tracking		http://blogs.oregonstate.edu/hess/code/particles/

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

UIUC同学Jia-Bin Huang收集的计算机视觉代码合集（ZZ）_ucizz

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