学步园

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

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

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

最后简单统计一下各个年份出现的频率

文章总数：372

2012年： 10

2011年： 20

2010年： 20

2009年： 14

2008年： 18

2007年： 13

2006年： 14

2005年： 9

2004年： 24

2003年： 22

2002年： 21

2001年： 21

2000年： 23

1999年： 10

1998年： 22

1997年： 8

1996年： 9

1995年： 9

1994年： 7

1993年： 5

1992年： 11

1991年： 5

1990年： 6

1980-1989： 22

1960-1979： 9

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

323个Item Type Topic Name Reference Link Code Structure from motion libmv   http://code.google.com/p/libmv/ Code Dimension Reduction LLE   http://www.cs.nyu.edu/~roweis/lle/code.html Code Clustering Spectral Clustering - UCSD Project   http://vision.ucsd.edu/~sagarwal/spectral-0.2.tgz Code Clustering K-Means - Oxford Code   http://www.cs.ucf.edu/~vision/Code/vggkmeans.zip Code Image Deblurring Non-blind deblurring (and blind denoising) with integrated noise estimation U. Schmidt, K. Schelten, and S. Roth. Bayesian deblurring with integrated noise estimation, CVPR 2011 http://www.gris.tu-darmstadt.de/research/visinf/software/index.en.htm Code Structure from motion Structure from Motion toolbox for Matlab by Vincent Rabaud   http://code.google.com/p/vincents-structure-from-motion-matlab-toolbox/ Code Multiple View Geometry Matlab Functions for Multiple View Geometry   http://www.robots.ox.ac.uk/~vgg/hzbook/code/ Code Object Detection Max-Margin Hough Transform S. Maji and J. Malik, Object Detection Using a Max-Margin Hough Transform. CVPR 2009 http://www.cs.berkeley.edu/~smaji/projects/max-margin-hough/ Code Image Segmentation SLIC Superpixels R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010 http://ivrg.epfl.ch/supplementary_material/RK_SLICSuperpixels/index.html Code Visual Tracking Tracking using Pixel-Wise Posteriors C. Bibby and I. Reid, Tracking using Pixel-Wise Posteriors, ECCV 2008 http://www.robots.ox.ac.uk/~cbibby/research_pwp.shtml Code Visual Tracking Visual Tracking with Histograms and Articulating Blocks S. M. Shshed Nejhum, J. Ho, and M.-H.Yang, Visual Tracking with Histograms and Articulating Blocks, CVPR 2008 http://www.cise.ufl.edu/~smshahed/tracking.htm Code Sparse Representation Robust Sparse Coding for Face Recognition M. Yang, L. Zhang, J. Yang and D. Zhang, “Robust Sparse Coding for Face Recognition,” CVPR 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/RSC.zip Code Feature Detection andFeature Extraction Groups of Adjacent Contour Segments V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, Groups of Adjacent Contour Segments for Object Detection, PAMI, 2007 http://www.robots.ox.ac.uk/~vgg/share/ferrari/release-kas-v102.tgz Code Density Estimation Kernel Density Estimation Toolbox   http://www.ics.uci.edu/~ihler/code/kde.html Code Illumination, Reflectance, and Shadow Ground shadow detection J.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer Photographs, ECCV 2010 http://www.jflalonde.org/software.html#shadowDetection Code Image Denoising,Image Super-resolution, andImage Deblurring Learning Models of Natural Image Patches D. Zoran and Y. Weiss, From Learning Models of Natural Image Patches to Whole Image Restoration, ICCV, 2011 http://www.cs.huji.ac.il/~daniez/ Code Illumination, Reflectance, and Shadow Estimating Natural Illumination from a Single Outdoor Image J-F. Lalonde, A. A. Efros, S. G. Narasimhan, Estimating Natural Illumination from a Single Outdoor Image , ICCV 2009 http://www.cs.cmu.edu/~jlalonde/software.html#skyModel Code Visual Tracking Lucas-Kanade affine template tracking S. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, IJCV 2002 http://www.mathworks.com/matlabcentral/fileexchange/24677-lucas-kanade-affine-template-tracking Code Saliency Detection Saliency-based video segmentation K. Fukuchi, K. Miyazato, A. Kimura, S. Takagi and J. Yamato, Saliency-based video segmentation with graph cuts and sequentially updated priors, ICME 2009 http://www.brl.ntt.co.jp/people/akisato/saliency3.html Code Dimension Reduction Laplacian Eigenmaps   http://www.cse.ohio-state.edu/~mbelkin/algorithms/Laplacian.tar Code Illumination, Reflectance, and Shadow What Does the Sky Tell Us About the Camera? J-F. Lalonde, S. G. Narasimhan, A. A. Efros, What Does the Sky Tell Us About the Camera?, ECCV 2008 http://www.cs.cmu.edu/~jlalonde/software.html#skyModel Code Image Filtering SVM for Edge-Preserving Filtering Q. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering, CVPR 2010 http://vision.ai.uiuc.edu/~qyang6/publications/code/cvpr-10-svmbf/program_video_conferencing.zip Code Image Segmentation Recovering Occlusion Boundaries from a Single Image D. Hoiem, A. Stein, A. A. Efros, M. Hebert, Recovering Occlusion Boundaries from a Single Image, ICCV 2007. http://www.cs.cmu.edu/~dhoiem/software/ Code Visual Tracking Visual Tracking Decomposition J Kwon and K. M. Lee, Visual Tracking Decomposition, CVPR 2010 http://cv.snu.ac.kr/research/~vtd/ Code Visual Tracking GPU Implementation of Kanade-Lucas-Tomasi Feature Tracker S. N Sinha, J.-M. Frahm, M. Pollefeys and Y. Genc, Feature Tracking and Matching in Video Using Programmable Graphics Hardware, MVA, 2007 http://cs.unc.edu/~ssinha/Research/GPU_KLT/ Code Object Detection Recognition using regions C. Gu, J. J. Lim, P. Arbelaez, and J. Malik, CVPR 2009 http://www.cs.berkeley.edu/~chunhui/publications/cvpr09_v2.zip Code Saliency Detection Saliency Using Natural statistics L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 2008 http://cseweb.ucsd.edu/~l6zhang/ Code Image Filtering Local Laplacian Filters S. Paris, S. Hasinoff, J. Kautz, Local Laplacian Filters: Edge-Aware Image Processing with a Laplacian Pyramid, SIGGRAPH 2011 http://people.csail.mit.edu/sparis/publi/2011/siggraph/matlab_source_code.zip Code Common Visual Pattern Discovery Sketching the Common S. Bagon, O. Brostovsky, M. Galun and M. Irani, Detecting and Sketching the Common, CVPR 2010 http://www.wisdom.weizmann.ac.il/~bagon/matlab_code/SketchCommonCVPR10_v1.1.tar.gz Code Image Denoising BLS-GSM   http://decsai.ugr.es/~javier/denoise/ Code Camera Calibration Epipolar Geometry Toolbox G.L. Mariottini, D. Prattichizzo, EGT: a Toolbox for Multiple View Geometry and Visual Servoing, IEEE Robotics & Automation Magazine, 2005 http://egt.dii.unisi.it/ Code Depth Sensor Kinect SDK http://www.microsoft.com/en-us/kinectforwindows/ http://www.microsoft.com/en-us/kinectforwindows/ Code Image Super-resolution Self-Similarities for Single Frame Super-Resolution C.-Y. Yang, J.-B. Huang, and M.-H. Yang, Exploiting Self-Similarities for Single Frame Super-Resolution, ACCV 2010 https://eng.ucmerced.edu/people/cyang35/ACCV10.zip Code Image Denoising Gaussian Field of Experts   http://www.cs.huji.ac.il/~yweiss/BRFOE.zip Code Object Detection Poselet L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations, ICCV 2009 http://www.eecs.berkeley.edu/~lbourdev/poselets/ Code Kernels and Distances Efficient Earth Mover's Distance with L1 Ground Distance (EMD_L1) H. Ling and K. Okada, An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison, PAMI 2007 http://www.dabi.temple.edu/~hbling/code/EmdL1_v3.zip Code Nearest Neighbors Matching Spectral Hashing Y. Weiss, A. Torralba, R. Fergus, Spectral Hashing, NIPS 2008 http://www.cs.huji.ac.il/~yweiss/SpectralHashing/ Code Image Denoising Field of Experts   http://www.cs.brown.edu/~roth/research/software.html Code Image Segmentation Multiscale Segmentation Tree E. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,” ACCV 2009 and N. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996 http://vision.ai.uiuc.edu/segmentation Code Multiple Instance Learning MILIS Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selection, PAMI 2010   Code Nearest Neighbors Matching FLANN: Fast Library for Approximate Nearest Neighbors   http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN Code Feature Detection andFeature Extraction Maximally stable extremal regions (MSER) - VLFeat J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. BMVC, 2002 http://www.vlfeat.org/ Code Alpha Matting Spectral Matting A. Levin, A. Rav-Acha, D. Lischinski. Spectral Matting. PAMI 2008 http://www.vision.huji.ac.il/SpectralMatting/ Code Multi-View Stereo Patch-based Multi-view Stereo Software Y. Furukawa and J. Ponce, Accurate, Dense, and Robust Multi-View Stereopsis, PAMI 2009 http://grail.cs.washington.edu/software/pmvs/ Code Clustering Self-Tuning Spectral Clustering   http://www.vision.caltech.edu/lihi/Demos/SelfTuningClustering.html Code Feature Extraction andObject Detection Histogram of Oriented Graidents - OLT for windows N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005 http://www.computing.edu.au/~12482661/hog.html Code Image Understanding Nonparametric Scene Parsing via Label Transfer C. Liu, J. Yuen, and Antonio Torralba, Nonparametric Scene Parsing via Label Transfer, PAMI 2011 http://people.csail.mit.edu/celiu/LabelTransfer/index.html Code Multiple Kernel Learning DOGMA F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learning. CVPR, 2010 http://dogma.sourceforge.net/ Code Distance Metric Learning Matlab Toolkit for Distance Metric Learning   http://www.cs.cmu.edu/~liuy/distlearn.htm Code Optical Flow Black and Anandan's Optical Flow   http://www.cs.brown.edu/~dqsun/code/ba.zip Code Text Recognition Text recognition in the wild K. Wang, B. Babenko, and S. Belongie, End-to-end Scene Text Recognition, ICCV 2011 http://vision.ucsd.edu/~kai/grocr/ Code MRF Optimization MRF Minimization Evaluation R. Szeliski et al., A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, PAMI, 2008 http://vision.middlebury.edu/MRF/ Code Saliency Detection Context-aware saliency detection S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, 2010. http://webee.technion.ac.il/labs/cgm/Computer-Graphics-Multimedia/Software/Saliency/Saliency.html Code Saliency Detection Learning to Predict Where Humans Look T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans Look, ICCV, 2009 http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html Code Stereo Stereo Evaluation D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV 2001 http://vision.middlebury.edu/stereo/ Code Image Segmentation Quick-Shift A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode Seeking, ECCV, 2008 http://www.vlfeat.org/overview/quickshift.html Code Saliency Detection Graph-based visual saliency J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007 http://www.klab.caltech.edu/~harel/share/gbvs.php Code Clustering K-Means - VLFeat   http://www.vlfeat.org/ Code Object Detection A simple object detector with boosting ICCV 2005 short courses on Recognizing and Learning Object Categories http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html Code Image Quality Assessment Structural SIMilarity   https://ece.uwaterloo.ca/~z70wang/research/ssim/ Code Structure from motion FIT3D   http://www.fit3d.info/ Code Image Denoising BM3D   http://www.cs.tut.fi/~foi/GCF-BM3D/ Code Saliency Detection Discriminant Saliency for Visual Recognition from Cluttered Scenes D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered Scenes, NIPS, 2004 http://www.svcl.ucsd.edu/projects/saliency/ Code Image Denoising Nonlocal means with cluster trees T. Brox, O. Kleinschmidt, D. Cremers, Efficient nonlocal means for denoising of textural patterns, TIP 2008 http://lmb.informatik.uni-freiburg.de/resources/binaries/nlmeans_brox_tip08Linux64.zip Code Saliency Detection Global Contrast based Salient Region Detection M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region Detection. CVPR, 2011 http://cg.cs.tsinghua.edu.cn/people/~cmm/saliency/ Code Visual Tracking Motion Tracking in Image Sequences C. Stauffer and W. E. L. Grimson. Learning patterns of activity using real-time tracking, PAMI, 2000 http://www.cs.berkeley.edu/~flw/tracker/ Code Saliency Detection Itti, Koch, and Niebur' saliency detection L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 1998 http://www.saliencytoolbox.net/ Code Feature Detection,Feature Extraction, andAction Recognition Space-Time Interest Points (STIP) I. Laptev, On Space-Time Interest Points, IJCV, 2005 and I. Laptev and T. Lindeberg, On Space-Time Interest Points, IJCV 2005 http://www.irisa.fr/vista/Equipe/People/Laptev/download/stip-1.1-winlinux.zip andhttp://www.nada.kth.se/cvap/abstracts/cvap284.html Code Texture Synthesis Image Quilting for Texture Synthesis and Transfer A. A. Efros and W. T. Freeman, Image Quilting for Texture Synthesis and Transfer, SIGGRAPH 2001 http://www.cs.cmu.edu/~efros/quilt_research_code.zip Code Image Denoising Non-local Means   http://dmi.uib.es/~abuades/codis/NLmeansfilter.m Code Low-Rank Modeling TILT: Transform Invariant Low-rank Textures Z. Zhang, A. Ganesh, X. Liang, and Y. Ma, TILT: Transform Invariant Low-rank Textures, IJCV 2011 http://perception.csl.uiuc.edu/matrix-rank/tilt.html Code Object Proposal Objectness measure B. Alexe, T. Deselaers, V. Ferrari, What is an Object?, CVPR 2010 http://www.vision.ee.ethz.ch/~calvin/objectness/objectness-release-v1.01.tar.gz Code Image Filtering Real-time O(1) Bilateral Filtering Q. Yang, K.-H. Tan and N. Ahuja, Real-time O(1) Bilateral Filtering, CVPR 2009 http://vision.ai.uiuc.edu/~qyang6/publications/code/qx_constant_time_bilateral_filter_ss.zip Code Image Quality Assessment SPIQA   http://vision.ai.uiuc.edu/~bghanem2/shared_code/SPIQA_code.zip Code Object Recognition Biologically motivated object recognition T. Serre, L. Wolf and T. Poggio. Object recognition with features inspired by visual cortex, CVPR 2005 http://cbcl.mit.edu/software-datasets/standardmodel/index.html Code Illumination, Reflectance, and Shadow Shadow Detection using Paired Region R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011 http://www.cs.illinois.edu/homes/guo29/projects/shadow.html Code Illumination, Reflectance, and Shadow Real-time Specular Highlight Removal Q. Yang, S. Wang and N. Ahuja, Real-time Specular Highlight Removal Using Bilateral Filtering, ECCV 2010 http://www.cs.cityu.edu.hk/~qiyang/publications/code/eccv-10.zip Code MRF Optimization Max-flow/min-cut Y. Boykov and V. Kolmogorov, An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, PAMI 2004 http://vision.csd.uwo.ca/code/maxflow-v3.01.zip Code Optical Flow Optical Flow Evaluation S. Baker et al. A Database and Evaluation Methodology for Optical Flow, IJCV, 2011 http://vision.middlebury.edu/flow/ Code Image Super-resolution MRF for image super-resolution W. T Freeman and C. Liu. Markov Random Fields for Super-resolution and Texture Synthesis. In A. Blake, P. Kohli, and C. Rother, eds., Advances in Markov Random Fields for Vision and Image Processing, Chapter 10. MIT Press, 2011 http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.html Code MRF Optimization Planar Graph Cut F. R. Schmidt, E. Toppe and D. Cremers, Efficient Planar Graph Cuts with Applications in Computer Vision, CVPR 2009 http://vision.csd.uwo.ca/code/PlanarCut-v1.0.zip Code Object Detection Feature Combination P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009 http://www.vision.ee.ethz.ch/~pgehler/projects/iccv09/index.html Code Structure from motion VisualSFM : A Visual Structure from Motion System   http://www.cs.washington.edu/homes/ccwu/vsfm/ Code Nearest Neighbors Matching ANN: Approximate Nearest Neighbor Searching   http://www.cs.umd.edu/~mount/ANN/ Code Saliency Detection Learning Hierarchical Image Representation with Sparsity, Saliency and Locality J. Yang and M.-H. Yang, Learning Hierarchical Image Representation with Sparsity, Saliency and Locality, BMVC 2011   Code Optical Flow Optical Flow by Deqing Sun D. Sun, S. Roth, M. J. Black, Secrets of Optical Flow Estimation and Their Principles, CVPR, 2010 http://www.cs.brown.edu/~dqsun/code/flow_code.zip Code Image Understanding Discriminative Models for Multi-Class Object Layout C. Desai, D. Ramanan, C. Fowlkes. "Discriminative Models for Multi-Class Object Layout, IJCV 2011 http://www.ics.uci.edu/~desaic/multiobject_context.zip Code Graph Matching Hyper-graph Matching via Reweighted Random Walks J. Lee, M. Cho, K. M. Lee. "Hyper-graph Matching via Reweighted Random Walks", CVPR 2011 http://cv.snu.ac.kr/research/~RRWHM/ Code Object Detection Hough Forests for Object Detection J. Gall and V. Lempitsky, Class-Specific Hough Forests for Object Detection, CVPR, 2009 http://www.vision.ee.ethz.ch/~gallju/projects/houghforest/index.html Code Object Discovery Using Multiple Segmentations to Discover Objects and their Extent in Image Collections B. Russell, A. A. Efros, J. Sivic, W. T. Freeman, A. Zisserman, Using Multiple Segmentations to Discover Objects and their Extent in Image Collections, CVPR 2006 http://people.csail.mit.edu/brussell/research/proj/mult_seg_discovery/index.html Code Dimension Reduction Diffusion maps   http://www.stat.cmu.edu/~annlee/software.htm Code Multiple Kernel Learning SHOGUN S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf . Large scale multiple kernel learning. JMLR, 2006 http://www.shogun-toolbox.org/ Code Distance Transformation Distance Transforms of Sampled Functions   http://people.cs.uchicago.edu/~pff/dt/ Code Image Filtering Image smoothing via L0 Gradient Minimization L. Xu, C. Lu, Y. Xu, J. Jia, Image smoothing via L0 Gradient Minimization, SIGGRAPH Asia 2011 http://www.cse.cuhk.edu.hk/~leojia/projects/L0smoothing/L0smoothing.zip Code Feature Extraction PCA-SIFT Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004 http://www.cs.cmu.edu/~yke/pcasift/ Code Visual Tracking Particle Filter Object Tracking   http://blogs.oregonstate.edu/hess/code/particles/ Code Feature Extraction sRD-SIFT M. Lourenco, J. P. Barreto and A. Malti, Feature Detection and Matching in Images with Radial Distortion, ICRA 2010 http://arthronav.isr.uc.pt/~mlourenco/srdsift/index.html# Code Multiple Instance Learning MILES Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection. PAMI 2006 http://infolab.stanford.edu/~wangz/project/imsearch/SVM/PAMI06/ Code Action Recognition Dense Trajectories Video Description H. Wang and A. Klaser and C. Schmid and C.- L. Liu, Action Recognition by Dense Trajectories, CVPR, 2011 http://lear.inrialpes.fr/people/wang/dense_trajectories Code Image Segmentation Efficient Graph-based Image Segmentation - C++ code P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation, IJCV 2004 http://people.cs.uchicago.edu/~pff/segment/ Code Object Proposal Parametric min-cut J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010 http://sminchisescu.ins.uni-bonn.de/code/cpmc/ Code Common Visual Pattern Discovery Common Visual Pattern Discovery via Spatially Coherent Correspondences H. Liu, S. Yan, "Common Visual Pattern Discovery via Spatially Coherent Correspondences", CVPR 2010 https://sites.google.com/site/lhrbss/home/papers/SimplifiedCode.zip?attredirects=0 Code Sparse Representation Sparse coding simulation software Olshausen BA, Field DJ, "Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images", Nature 1996 http://redwood.berkeley.edu/bruno/sparsenet/ Code MRF Optimization Max-flow/min-cut for massive grids A. Delong and Y. Boykov, A Scalable Graph-Cut Algorithm for N-D Grids, CVPR 2008 http://vision.csd.uwo.ca/code/regionpushrelabel-v1.03.zip Code Optical Flow Horn and Schunck's Optical Flow   http://www.cs.brown.edu/~dqsun/code/hs.zip Code Sparse Representation Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing http://www.cs.technion.ac.il/~elad/Various/Matlab-Package-Book.rar Code Image Understanding Towards Total Scene Understanding L.-J. Li, R. Socher and Li F.-F.. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework, CVPR 2009 http://vision.stanford.edu/projects/totalscene/index.html Code Camera Calibration Camera Calibration Toolbox for Matlab http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/ref.html http://www.vision.caltech.edu/bouguetj/calib_doc/ Code Image Segmentation Turbepixels A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric Flows, PAMI 2009 http://www.cs.toronto.edu/~babalex/research.html Code Feature Detection Edge Foci Interest Points L. Zitnickand K. Ramnath, Edge Foci Interest Points, ICCV, 2011 http://research.microsoft.com/en-us/um/people/larryz/edgefoci/edge_foci.htm Code Feature Extraction Local Self-Similarity Descriptor E. Shechtman and M. Irani. Matching local self-similarities across images and videos, CVPR, 2007 http://www.robots.ox.ac.uk/~vgg/software/SelfSimilarity/ Code Subspace Learning Generalized Principal Component Analysis R. Vidal, Y. Ma and S. Sastry. Generalized Principal Component Analysis (GPCA), CVPR 2003 http://www.vision.jhu.edu/downloads/main.php?dlID=c1 Code Camera Calibration EasyCamCalib J. Barreto, J. Roquette, P. Sturm, and F. Fonseca, Automatic camera calibration applied to medical endoscopy, BMVC, 2009 http://arthronav.isr.uc.pt/easycamcalib/ Code Image Segmentation Superpixel by Gerg Mori X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003 http://www.cs.sfu.ca/~mori/research/superpixels/ Code Image Understanding Object Bank Li-Jia Li, Hao Su, Eric P. Xing and Li Fei-Fei. Object Bank: A High-Level Image Representation for Scene Classification and Semantic Feature Sparsification, NIPS 2010 http://vision.stanford.edu/projects/objectbank/index.html Code Saliency Detection Spectrum Scale Space based Visual Saliency J Li, M D. Levine, X An and H. He, Saliency Detection Based on Frequency and Spatial Domain Analyses, BMVC 2011 http://www.cim.mcgill.ca/~lijian/saliency.htm Code Sparse Representation Fisher Discrimination Dictionary Learning for Sparse Representation M. Yang, L. Zhang, X. Feng and D. Zhang, Fisher Discrimination Dictionary Learning for Sparse Representation, ICCV 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/FDDL.zip Code Object Detection Cascade Object Detection with Deformable Part Models P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part Models. CVPR, 2010 http://people.cs.uchicago.edu/~rbg/star-cascade/ Code Object Segmentation Sparse to Dense Labeling P. Ochs, T. Brox, Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions, ICCV 2011 http://lmb.informatik.uni-freiburg.de/resources/binaries/SparseToDenseLabeling.tar.gz Code Optical Flow Dense Point Tracking N. Sundaram, T. Brox, K. KeutzerDense point trajectories by GPU-accelerated large displacement optical flow, ECCV 2010 http://lmb.informatik.uni-freiburg.de/resources/binaries/ Code Visual Tracking Tracking with Online Multiple Instance Learning B. Babenko, M.-H. Yang, S. Belongie, Visual Tracking with Online Multiple Instance Learning, PAMI 2011 http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml Code Graph Matching Reweighted Random Walks for Graph Matching M. Cho, J. Lee, and K. M. Lee, Reweighted Random Walks for Graph Matching, ECCV 2010 http://cv.snu.ac.kr/research/~RRWM/ Code Machine Learning Statistical Pattern Recognition Toolbox M.I. Schlesinger, V. Hlavac: Ten lectures on the statistical and structural pattern recognition, Kluwer Academic Publishers, 2002 http://cmp.felk.cvut.cz/cmp/software/stprtool/ Code Image Super-resolution Sprarse coding super-resolution J. Yang, J. Wright, T. S. Huang, and Y. Ma. Image super-resolution via sparse representation, TIP 2010 http://www.ifp.illinois.edu/~jyang29/ScSR.htm Code Object Detection Discriminatively Trained Deformable Part Models P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan.Object Detection with Discriminatively Trained Part Based Models, PAMI, 2010 http://people.cs.uchicago.edu/~pff/latent/ Code Multiple Instance Learning MIForests C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized Trees, ECCV 2010 http://www.ymer.org/amir/software/milforests/ Code Optical Flow Large Displacement Optical Flow T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimation, PAMI 2011 http://lmb.informatik.uni-freiburg.de/resources/binaries/ Code Multiple View Geometry MATLAB and Octave Functions for Computer Vision and Image Processing P. D. Kovesi. MATLAB and Octave Functions for Computer Vision and Image Processing, http://www.csse.uwa.edu.au/~pk/research/matlabfns http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.html Code Image Filtering Anisotropic Diffusion P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, PAMI 1990 http://www.mathworks.com/matlabcentral/fileexchange/14995-anisotropic-diffusion-perona-malik Code Feature Detection andFeature Extraction Geometric Blur A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR, 2005 http://www.robots.ox.ac.uk/~vgg/software/MKL/ Code Low-Rank Modeling Low-Rank Matrix Recovery and Completion   http://perception.csl.uiuc.edu/matrix-rank/sample_code.html Code Object Detection A simple parts and structure object detector ICCV 2005 short courses on Recognizing and Learning Object Categories http://people.csail.mit.edu/fergus/iccv2005/partsstructure.html Code Kernels and Distances Diffusion-based distance H. Ling and K. Okada, Diffusion Distance for Histogram Comparison, CVPR 2006 http://www.dabi.temple.edu/~hbling/code/DD_v1.zip Code Image Denoising K-SVD   http://www.cs.technion.ac.il/~ronrubin/Software/ksvdbox13.zip Code Multiple Kernel Learning SimpleMKL A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. Simplemkl. JMRL, 2008 http://asi.insa-rouen.fr/enseignants/~arakotom/code/mklindex.html Code Feature Extraction Pyramids of Histograms of Oriented Gradients (PHOG) A. Bosch, A. Zisserman, and X. Munoz, Representing shape with a spatial pyramid kernel, CIVR, 2007 http://www.robots.ox.ac.uk/~vgg/research/caltech/phog/phog.zip Code Sparse Representation Efficient sparse coding algorithms H. Lee, A. Battle, R. Rajat and A. Y. Ng, Efficient sparse coding algorithms, NIPS 2007 http://ai.stanford.edu/~hllee/softwares/nips06-sparsecoding.htm Code Multi-View Stereo Clustering Views for Multi-view Stereo Y. Furukawa, B. Curless, S. M. Seitz, and R. Szeliski, Towards Internet-scale Multi-view Stereo, CVPR 2010 http://grail.cs.washington.edu/software/cmvs/ Code Multi-View Stereo Multi-View Stereo Evaluation S. Seitz et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006 http://vision.middlebury.edu/mview/ Code Structure from motion Structure and Motion Toolkit in Matlab   http://cms.brookes.ac.uk/staff/PhilipTorr/Code/code_page_4.htm Code Pose Estimation Training Deformable Models for Localization Ramanan, D. "Learning to Parse Images of Articulated Bodies." NIPS 2006 http://www.ics.uci.edu/~dramanan/papers/parse/index.html Code Low-Rank Modeling RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition Y. Peng, A. Ganesh, J. Wright, W. Xu, and Y. Ma, RASL: Robust Batch Alignment of Images by Sparse and Low-Rank Decomposition, CVPR 2010 http://perception.csl.uiuc.edu/matrix-rank/rasl.html Code Dimension Reduction ISOMAP   http://isomap.stanford.edu/ Code Alpha Matting Learning-based Matting Y. Zheng and C. Kambhamettu, Learning Based Digital Matting, ICCV 2009 http://www.mathworks.com/matlabcentral/fileexchange/31412 Code Image Segmentation Normalized Cut J. Shi and J Malik, Normalized Cuts and Image Segmentation, PAMI, 2000 http://www.cis.upenn.edu/~jshi/software/ Code Image Denoising andStereo Matching Efficient Belief Propagation for Early Vision P. F. Felzenszwalb and D. P. Huttenlocher, Efficient Belief Propagation for Early Vision, IJCV, 2006 http://www.cs.brown.edu/~pff/bp/ Code Sparse Representation A Linear Subspace Learning Approach via Sparse Coding L. Zhang, P. Zhu, Q. Hu and D. Zhang, “A Linear Subspace Learning Approach via Sparse Coding,” ICCV 2011 http://www4.comp.polyu.edu.hk/~cslzhang/code/LSL_SC.zip Code Text Recognition Neocognitron for handwritten digit recognition K. Fukushima: "Neocognitron for handwritten digit recognition", Neurocomputing, 2003 返回 【上篇】美媒：小米新浪达成合作 采取行动对抗腾讯 【下篇】网页制作（七）—照着google注册页面边学边写的一个网页 作者: picket 该日志由 picket 于11年前发表在综合分类下，最后更新于 2013年08月02日. 转载请注明: 图像处理与计算机视觉资源汇总——论文+代码+教材+视频等等 | 学步园 +复制链接 抱歉!评论已关闭. 书签 招生 白云飘飘网 青岛房产网 最新文章New 网站优化可以收获更好的收益 robots在网页开发中起到了一定的 SEO引擎优化可以更好的方便搜索 Dreamweaver教程很实用，值得学习 很多的photoshop教程值得学习，你 编程语言很重要，特别是对于计算 数据库非常常见，也非常实用！ H5指的是第5代html，不同于传统企 HTML是什么，该怎么制作？ 本站推荐 为什么PHP的吉祥物是一头大象 作业的提交和监控（二） 作业的提交和监控（一） Boost – Function 分析 奇技淫巧 – C/C++ 宏自身 模板的 SFINAE 原则 Octopress 和 Git 的结合 Electric-fence 介绍 web前端 数据库 编程语言 搜索技术 关于本站 返回首页 Copyright © 2013-2018 学步园  保留所有权利. 软文销售 QQ客服：2265327166 （其他合作也可洽谈） 必威体育 必威电竞