一直在研究图像处理方面的东西，发现了几个比较全面的图像处理方面的项目代码集，主要是 关于Feature Extraction、Image Segmentation、Object Detection、Image Classification, Clustering、Image Matting、Object Tracking以及Machine Learning相关的知识。里面含有比较基础的理论知识，分享给大家参考参考：

1、大咖的主页（附代码喲）

Serge Belongie at UC San Diego

Antonio Torralba at MIT

Alexei Ffros at CMU

Ce Liu at Microsoft Research New England

Vittorio Ferrari at Univ.of Edinburgh

Kristen Grauman at UT Austin

Devi Parikh at  TTI-Chicago (Marr Prize at ICCV2011)

John Wright at Columbia Univ.

Piotr Dollar at CalTech

Boris Babenko at UC San Diego

David Ross at Google/Youtube

Terence Tao at UCLA

David Donoho at Stanford Univ.

William T. Freeman at MIT

Roberto Cipolla at Cambridge

David Lowe at Univ. of British Columbia

Mubarak Shah at Univ. of Central Florida

Yi Ma at MSRA

Tinne Tuytelaars at K.U. Leuven

Trevor Darrell at U.C. Berkeley

Michael J. Black at Brown Univ.

朱松纯 （Song-Chun Zhu）

http://www.stat.ucla.edu/~sczhu/

David Lowe (SIFT) 

http://www.cs.ubc.ca/~lowe/

Andrea Vedaldi (SIFT)

http://vision.ucla.edu/~vedaldi/index.html

Pedro F. Felzenszwalb

http://people.cs.uchicago.edu/~pff/ 

Dougla Dlanman (Brown的一个研究生，在其主页上搜集了大量算法教程和源码)

http://mesh.brown.edu/dlanman/courses.html

Jianbo Shi (Ncuts 的始作俑者)

http://www.cis.upenn.edu/~jshi/

Active Vision Group (Oxford的一个机器视觉研究团队，特色是SLAM，监视，导航)

http://www.robots.ox.ac.uk/ActiveVision/index.html

Juyang Weng（机器学习的专家，Autonomous Mental Development 是其特色）

http://www.cse.msu.edu/~weng/

2、重要研究组

Computer Vision Group at UC Berkeley

Robotics Research Group at Univ. of Oxford

LEAR at INRIA

Computer Vision Lab at Stanford

Computer Vision Lab at EPFL

Computer Vision Lab at ETH Zurich

Computer Vision Lab at Seoul National Univ.

Computer Vision Lab at UC San Diego

Computer Vision Lab at UC Santa Cruz

Computer Vision Lab at Univ. of Southern California

Computer Vision Lab at Univ. of Central Florida

Computer Vision Lab at Columbia Univ.

UCLA Vision Lab

Motion and Shape Computing Group at George Mason Univ.

Robust Image Understanding Lab at Rutgers Univ.

Intelligent Vision Systems Group at Univ. of Bonn

Institute for Computer Graphics and Vision at Graz Univ. of Tech.

Computer Vision Lab. at Vienna Univ. of Tech. 

Computational Image Analysis and Radiology at Medical Univ. of Vienna

Personal Robotics Lab at CMU

Visual Perception Lab at Purdue Univ.

3、潜力牛人

Juergen Gall at ETH Zurich

Matt Flagg at Georgia Tech.

Mathieu Salzmann at TTI-Chicago

Gerg Shakhnarovich at TTI-Chicago

Taeg Sang Cho at MIT

Jianchao Yang at UIUC

Stefan Roth at TU Darmstadt

Peter Kontschieder at Graz Univ. of Tech.

Dominik Alexander Klein at Univ. of Bonn

Yinan Yu at CASIA (PASCAL VOC 2010 Detection Challenge Winner)

Zdenek Kalal at FPFL

Julien Pilet at FPFL

Kenji Okuma

（1）googleResearch： http://research.google.com/index.html
（2）MIT博士，汤晓欧学生林达华： http://people.csail.mit.edu/dhlin/index.html
（3）MIT博士后Douglas Lanman： http://web.media.mit.edu/~dlanman/
（4）opencv中文网站：http://www.opencv.org.cn/index.php/首页
（5）Stanford大学vision实验室： http://vision.stanford.edu/research.html
（6）Stanford大学博士崔靖宇： http://www.stanford.edu/~jycui/
（7）UCLA教授朱松纯： http://www.stat.ucla.edu/~sczhu/
（8）中国人工智能网： http://www.chinaai.org/
（9）中国视觉网： http://www.china-vision.net/
（10）中科院自动化所： http://www.ia.cas.cn/
（11）中科院自动化所李子青研究员： http://www.cbsr.ia.ac.cn/users/szli/
（12）中科院计算所山世光研究员： http://www.jdl.ac.cn/user/sgshan/
（13）人脸识别主页：http://www.face-rec.org/
（14）加州大学伯克利分校CV小组： http://www.eecs.berkeley.edu/Research/Projects/CS/vision/

（15）南加州大学CV实验室：http://iris.usc.edu/USC-Computer-Vision.html
（16）卡内基梅隆大学CV主页：http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html

（17）微软CV研究员Richard Szeliski：http://research.microsoft.com/en-us/um/people/szeliski/
（18）微软亚洲研究院计算机视觉研究组： http://research.microsoft.com/en-us/groups/vc/
（19）微软剑桥研究院ML与CV研究组：http://research.microsoft.com/en-us/groups/mlp/default.aspx

（20）研学论坛： http://bbs.matwav.com/
（21）美国Rutgers大学助理教授刘青山；http://www.research.rutgers.edu/~qsliu/
（22）计算机视觉最新资讯网； http://www.cvchina.info/
（23）运动检测、阴影、跟踪的测试视频下载；http://apps.hi.baidu.com/share/detail/18903287
（24）香港中文大学助理教授王晓刚； http://www.ee.cuhk.edu.hk/~xgwang/
(25)香港中文大学多媒体实验室（汤晓鸥）; http://mmlab.ie.cuhk.edu.hk/
(26)U.C. San Diego. computer vision;http://vision.ucsd.edu/content/home
(27)CVonline; http://homepages.inf.ed.ac.uk/rbf/CVonline/
(28)computer vision software; http://peipa.essex.ac.uk/info/software.html
(29)Computer Vision Resource; http://www.cvpapers.com/
(30)computer vision research groups;http://peipa.essex.ac.uk/info/groups.html
(31)computer vision center; http://computervisioncentral.com/cvcnews

(32)浙江大学图像技术研究与应用（ITRA）团队：http://www.dvzju.com/

(33)自动识别网：http://www.autoid-china.com.cn/

(34)清华大学章毓晋教授：

http://www.tsinghua.edu.cn/publish/ee/4157/2010/20101217173552339241557/20101217173552339241557_.html

(35)顶级民用机器人研究小组Porf.Gary领导的Willow Garage:http://www.willowgarage.com/

(36)上海交通大学图像处理与模式识别研究所：http://www.pami.sjtu.edu.cn/

(37)上海交通大学计算机视觉实验室刘允才教授：http://www.visionlab.sjtu.edu.cn/

(38)德克萨斯州大学奥斯汀分校助理教授Kristen Grauman ：http://www.cs.utexas.edu/~grauman/

(39)清华大学电子工程系智能图文信息处理实验室（丁晓青教授）：http://ocrserv.ee.tsinghua.edu.cn/auto/index.asp

(40)北京大学高文教授：http://www.jdl.ac.cn/htm-gaowen/

(41)清华大学艾海舟教授：http://media.cs.tsinghua.edu.cn/cn/aihz

(42)中科院生物识别与安全技术研究中心：http://www.cbsr.ia.ac.cn/china/index CH.asp

(43)瑞士巴塞尔大学 Thomas Vetter教授：http://informatik.unibas.ch/personen/vetter_t.html

(44)俄勒冈州立大学 Rob Hess博士：http://blogs.oregonstate.edu/hess/

(45)深圳大学 于仕祺副教授：http://yushiqi.cn/

(46)西安交通大学人工智能与机器人研究所：http://www.aiar.xjtu.edu.cn/

(47)卡内基梅隆大学研究员Robert T. Collins:http://www.cs.cmu.edu/~rcollins/home.html#Background

(48)MIT博士Chris Stauffer:http://people.csail.mit.edu/stauffer/Home/index.php

(49)美国密歇根州立大学生物识别研究组(Anil K. Jain教授)：http://www.cse.msu.edu/rgroups/biometrics/

(50)美国伊利诺伊州立大学Thomas S. Huang:http://www.beckman.illinois.edu/directory/t-huang1

(51)武汉大学数字摄影测量与计算机视觉研究中心：http://www.whudpcv.cn/index.asp

(52)瑞士巴塞尔大学Sami Romdhani助理研究员：http://informatik.unibas.ch/personen/romdhani_sami/

(53)CMU大学研究员Yang Wang:http://www.cs.cmu.edu/~wangy/home.html

(54)英国曼彻斯特大学Tim Cootes教授：http://personalpages.manchester.ac.uk/staff/timothy.f.cootes/

(55)美国罗彻斯特大学教授Jiebo Luo:http://www.cs.rochester.edu/u/jluo/

(56)美国普渡大学机器人视觉实验室：https://engineering.purdue.edu/RVL/Welcome.html

(57)美国宾利州立大学感知、运动与认识实验室：http://vision.cse.psu.edu/home/home.shtml

(58)美国宾夕法尼亚大学GRASP实验室：https://www.grasp.upenn.edu/

(59)美国内达华大学里诺校区CV实验室：http://www.cse.unr.edu/CVL/index.php

(60)美国密西根大学vision实验室：http://www.eecs.umich.edu/vision/index.html

(61)University of Massachusetts(麻省大学),视觉实验室：http://vis-www.cs.umass.edu/index.html

(62)华盛顿大学博士后Iva Kemelmacher:http://www.cs.washington.edu/homes/kemelmi

(63)以色列魏茨曼科技大学Ronen Basri:http://www.wisdom.weizmann.ac.il/~ronen/index.html

(64)瑞士ETH-Zurich大学CV实验室：http://www.vision.ee.ethz.ch/boostingTrackers/index.htm

(65)微软CV研究员张正友：http://research.microsoft.com/en-us/um/people/zhang/

(66)中科院自动化所医学影像研究室：http://www.3dmed.net/

(67)中科院田捷研究员：http://www.3dmed.net/tian/

(68)微软Redmond研究院研究员Simon Baker:http://research.microsoft.com/en-us/people/sbaker/

(69)普林斯顿大学教授李凯：http://www.cs.princeton.edu/~li/
(70)普林斯顿大学博士贾登：http://www.cs.princeton.edu/~jiadeng/
(71)牛津大学教授Andrew Zisserman： http://www.robots.ox.ac.uk/~az/
(72)英国leeds大学研究员Mark Everingham:http://www.comp.leeds.ac.uk/me/
(73)英国爱丁堡大学教授Chris William: http://homepages.inf.ed.ac.uk/ckiw/
(74)微软剑桥研究院研究员John Winn: http://johnwinn.org/
(75)佐治亚理工学院教授Monson H.Hayes：http://savannah.gatech.edu/people/mhayes/index.html
(76)微软亚洲研究院研究员孙剑：http://research.microsoft.com/en-us/people/jiansun/
(77)微软亚洲研究院研究员马毅：http://research.microsoft.com/en-us/people/mayi/
(78)英国哥伦比亚大学教授David Lowe: http://www.cs.ubc.ca/~lowe/
(79)英国爱丁堡大学教授Bob Fisher: http://homepages.inf.ed.ac.uk/rbf/
(80)加州大学圣地亚哥分校教授Serge J.Belongie:http://cseweb.ucsd.edu/~sjb/
(81)威斯康星大学教授Charles R.Dyer: http://pages.cs.wisc.edu/~dyer/
(82)多伦多大学教授Allan.Jepson: http://www.cs.toronto.edu/~jepson/
(83)伦斯勒理工学院教授Qiang Ji: http://www.ecse.rpi.edu/~qji/
(84)CMU研究员Daniel Huber: http://www.ri.cmu.edu/person.html?person_id=123
(85)多伦多大学教授：David J.Fleet: http://www.cs.toronto.edu/~fleet/
(86)伦敦大学玛丽女王学院教授Andrea Cavallaro:http://www.eecs.qmul.ac.uk/~andrea/
(87)多伦多大学教授Kyros Kutulakos: http://www.cs.toronto.edu/~kyros/
(88)杜克大学教授Carlo Tomasi: http://www.cs.duke.edu/~tomasi/
(89)CMU教授Martial Hebert: http://www.cs.cmu.edu/~hebert/
(90)MIT助理教授Antonio Torralba: http://web.mit.edu/torralba/www/
(91)马里兰大学研究员Yasel Yacoob: http://www.umiacs.umd.edu/users/yaser/
(92)康奈尔大学教授Ramin Zabih: http://www.cs.cornell.edu/~rdz/

(93)CMU博士田渊栋: http://www.cs.cmu.edu/~yuandong/
(94)CMU副教授Srinivasa Narasimhan: http://www.cs.cmu.edu/~srinivas/
(95)CMU大学ILIM实验室：http://www.cs.cmu.edu/~ILIM/
(96)哥伦比亚大学教授Sheer K.Nayar: http://www.cs.columbia.edu/~nayar/
(97)三菱电子研究院研究员Fatih Porikli ：http://www.porikli.com/
(98)康奈尔大学教授Daniel Huttenlocher：http://www.cs.cornell.edu/~dph/
(99)南京大学教授周志华：http://cs.nju.edu.cn/zhouzh/index.htm
(100)芝加哥丰田技术研究所助理教授Devi Parikh: http://ttic.uchicago.edu/~dparikh/index.html
(101)瑞士联邦理工学院博士后Helmut Grabner: http://www.vision.ee.ethz.ch/~hegrabne/#Short_CV

(102)香港中文大学教授贾佳亚：http://www.cse.cuhk.edu.hk/~leojia/index.html

(103)南洋理工大学副教授吴建鑫：http://c2inet.sce.ntu.edu.sg/Jianxin/index.html

(104)GE研究院研究员李关：http://www.cs.unc.edu/~lguan/

(105)佐治亚理工学院教授Monson Hayes:http://savannah.gatech.edu/people/mhayes/

(106)图片检索国际会议VOC(微软剑桥研究院组织):http://pascallin.ecs.soton.ac.uk/challenges/VOC/

(107)机器视觉开源处理库汇总：http://archive.cnblogs.com/a/2217609/

(108)布朗大学教授Benjamin Kimia: http://www.lems.brown.edu/kimia.html 

about multi-camera: http://server.cs.ucf.edu/~vision/projects.html

about 3D Voxel Coloring   Rob Hess: http://blogs.oregonstate.edu/hess/code/voxels/ 

About  the particle filters--condensation filter:

http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/ISARD1/condensation.html

 Machine Learning Open Source Software：http://jmlr.csail.mit.edu/mloss/

 1、动作识别数据库：Recognition of human actions：http://www.nada.kth.se/cvap/actions/

 2、Datasets for Computer Vision Research：http://www-cvr.ai.uiuc.edu/ponce_grp/data/

 3、Computer Vision Datasets:http://clickdamage.com/sourcecode/cv_datasets.php

 4、里面有好多基本算法 matlab：  http://www.mathworks.cn/index.html

 5、CVPR 2011中关于grassmann 流形文章的源码： http://itee.uq.edu.au/~uqmhara1/code.html

 Matlab Codefor Graph Embedding Discriminant Analysis on Grassmannian Manifolds for Improved Image Set Matching (CVPR), 2011.

• Matlab Codefor Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition(IJCV), vol. 81, no. 2, pp. 191-204, 2009.

4、 大咖博客：

1、Hong Kong Polytechnic University ：http://www4.comp.polyu.edu.hk/~cslzhang/

2、Computer Vision Resources：资源非常丰富，包含有基本算法。

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

3、源代码非常丰富~~  http://homepage.tudelft.nl/19j49/Publications.html

 4、Xiaofei He(machine learning code)

http://people.cs.uchicago.edu/~xiaofei/

 5、YingNian Wu(active base model code)

http://www.stat.ucla.edu/~ywu/research.html

 6、布朗大学计算机主页（可找到该校CS牛人博客）

http://www.cs.brown.edu/research/areas.html

7、Navneet Dalal(Histograms of Oriented Gradients for Human Detection )

http://www.navneetdalal.com/software

8、Paul Viola(Robust Real-time Object Detection)

http://research.microsoft.com/en-us/um/people/viola/ 

5、CV online

http://homepages.inf.ed.ac.uk/rbf/CVonline

http://homepages.inf.ed.ac.uk/rbf/CVonline/unfolded.htm

http://homepages.inf.ed.ac.uk/rbf/CVonline/CVentry.htm 

李子青的大作：

Markov Random Field Modeling in Computer Vision

http://www.cbsr.ia.ac.cn/users/szli/mrf_book/book.html

Handbook of Face Recognition (PDF)

http://www.umiacs.umd.edu/~shaohua/papers/zhou04hfr.pdf 

张正友的有关参数鲁棒估计著作：

Parameter Estimation Techniques:A Tutorial with Application to Conic Fitting

http://research.microsoft.com/~zhang/INRIA/Publis/Tutorial-Estim/Main.html

Andrea Fusiello“计算机视觉中的几何”教程：Elements of Geometric Computer Vision

http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/FUSIELLO4/tutorial.html#x1-520007 

有关马尔可夫蒙特卡罗方法的资料：

An introduction to Markov chain Monte Carlo

http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/SENEGAS/mcmc.html

Markov Chain Monte Carlo for Computer Vision--- A tutorial at ICCV05

       http://civs.stat.ucla.edu/MCMC/MCMC_tutorial.htm 

有关独立成分分析（Independent Component Analysis , ICA）的资料：

An ICA-Page

http://www.cnl.salk.edu/~tony/ica.html

Fast ICA

http://www.cis.hut.fi/projects/ica/fastica/

The Kalman Filter (介绍卡尔曼滤波器的终极网页)

      http://www.cs.unc.edu/~welch/kalman/index.html

Cached k-d tree search for ICP algorithms

http://kos.informatik.uni-osnabrueck.de/download/3dim2007/paper.html

6、几个计算机视觉研究工具

Machine Vision Toolbox for Matlab

http://www.petercorke.com/Machine Vision Toolbox.html

Matlab and Octave Function for Computer Vision and Image Processing

http://www.csse.uwa.edu.au/~pk/research/matlabfns/

Bayes Net Toolbox for Matlab

http://www.cs.ubc.ca/~murphyk/Software/BNT/bnt.html

OpenCV (Chinese)

http://www.opencv.org.cn/index.php/首页

Gandalf (A Computer Vision and Numerical Algorithm Labrary)

http://gandalf-library.sourceforge.net/

CMU Computer Vision Home Page

http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html

Machine Learning Resource Links

http://www.cse.ust.hk/~ivor/resource.htm

The Bayesian Filtering Library

http://www.orocos.org/bfl

Optical Flow Algorithm Evaluation (提供了一个动态贝叶斯网络框架，例如递归信息处理与分析、卡尔曼滤波、粒子滤波、序列蒙特卡罗方法等，C++写的)

http://of-eval.sourceforge.net/ 

MATLAB code for ICP algorithm

http://www.usenet.com/newsgroups/comp.graphics.visualization/msg00102.html

7、测试图像或视频

Middlebury College‘s Stereo Vision Data Set

http://cat.middlebury.edu/stereo/data.html

Intelligent Vehicle:

IVSource

www.ivsoruce.net

Robot Car

http://www.plyojump.com/robot_cars.html

How to Build a Robot: The Computer Vision Part

http://www.societyofrobots.com/programming_computer_vision_tutorial.shtml

以下内容涵盖了Feature Extraction、Image Segmentation、Object Detection、Image Classification, Clustering、Image Matting、Object Tracking等方面的项目和代码，以及有些工具可以参考使用：

一、特征提取Feature Extraction：
   SIFT [1] [Demo program][SIFT Library] [VLFeat]
   PCA-SIFT [2] [Project]
   Affine-SIFT [3] [Project]
   SURF [4] [OpenSURF] [Matlab Wrapper]
   Affine Covariant Features [5] [Oxford project]
   MSER [6] [Oxford project] [VLFeat]
   Geometric Blur [7] [Code]
   Local Self-Similarity Descriptor [8] [Oxford implementation]
   Global and Efficient Self-Similarity [9] [Code]
   Histogram of Oriented Graidents [10] [INRIA Object Localization Toolkit] [OLT toolkit for Windows]
   GIST [11] [Project]
   Shape Context [12] [Project]
   Color Descriptor [13] [Project]
   Pyramids of Histograms of Oriented Gradients [Code]
   Space-Time Interest Points (STIP) [14][Project] [Code]
   Boundary Preserving Dense Local Regions [15][Project]
   Weighted Histogram[Code]
   Histogram-based Interest Points Detectors[Paper][Code]
   An OpenCV - C++ implementation of Local Self Similarity Descriptors [Project]
   Fast Sparse Representation with Prototypes[Project]
   Corner Detection [Project]
   AGAST Corner Detector: faster than FAST and even FAST-ER[Project]
二、图像分割Image Segmentation：
     Normalized Cut [1] [Matlab code]
     Gerg Mori’ Superpixel code [2] [Matlab code]
     Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]
     Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper]
     OWT-UCM Hierarchical Segmentation [5] [Resources]
     Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code]
     Quick-Shift [7] [VLFeat]
     SLIC Superpixels [8] [Project]
     Segmentation by Minimum Code Length [9] [Project]
     Biased Normalized Cut [10] [Project]
     Segmentation Tree [11-12] [Project]
     Entropy Rate Superpixel Segmentation [13] [Code]
     Fast Approximate Energy Minimization via Graph Cuts[Paper][Code]
     Efficient Planar Graph Cuts with Applications in Computer Vision[Paper][Code]
     Isoperimetric Graph Partitioning for Image Segmentation[Paper][Code]
     Random Walks for Image Segmentation[Paper][Code]
     Blossom V: A new implementation of a minimum cost perfect matching algorithm[Code]
     An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision[Paper][Code]
     Geodesic Star Convexity for Interactive Image Segmentation[Project]
     Contour Detection and Image Segmentation Resources[Project][Code]
     Biased Normalized Cuts[Project]
     Max-flow/min-cut[Project]
     Chan-Vese Segmentation using Level Set[Project]
     A Toolbox of Level Set Methods[Project]
     Re-initialization Free Level Set Evolution via Reaction Diffusion[Project]
     Improved C-V active contour model[Paper][Code]
     A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction[Paper][Code]
    Level Set Method Research by Chunming Li[Project]
三、目标检测Object Detection：
     A simple object detector with boosting [Project]
     INRIA Object Detection and Localization Toolkit [1] [Project]
     Discriminatively Trained Deformable Part Models [2] [Project]
     Cascade Object Detection with Deformable Part Models [3] [Project]
     Poselet [4] [Project]
     Implicit Shape Model [5] [Project]
     Viola and Jones’s Face Detection [6] [Project]
     Bayesian Modelling of Dyanmic Scenes for Object Detection[Paper][Code]
     Hand detection using multiple proposals[Project]
     Color Constancy, Intrinsic Images, and Shape Estimation[Paper][Code]
     Discriminatively trained deformable part models[Project]
     Gradient Response Maps for Real-Time Detection of Texture-Less Objects: LineMOD [Project]
     Image Processing On Line[Project]
     Robust Optical Flow Estimation[Project]
     Where's Waldo: Matching People in Images of Crowds[Project]
四、显著性检测Saliency Detection：
     Itti, Koch, and Niebur’ saliency detection [1] [Matlab code]
     Frequency-tuned salient region detection [2] [Project]
     Saliency detection using maximum symmetric surround [3] [Project]
     Attention via Information Maximization [4] [Matlab code]
     Context-aware saliency detection [5] [Matlab code]
     Graph-based visual saliency [6] [Matlab code]
     Saliency detection: A spectral residual approach. [7] [Matlab code]
     Segmenting salient objects from images and videos. [8] [Matlab code]
     Saliency Using Natural statistics. [9] [Matlab code]
     Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] [Code]
     Learning to Predict Where Humans Look [11] [Project]
     Global Contrast based Salient Region Detection [12] [Project]
     Bayesian Saliency via Low and Mid Level Cues[Project]
     Top-Down Visual Saliency via Joint CRF and Dictionary Learning[Paper][Code]
五、图像分类、聚类Image Classification, Clustering
     Pyramid Match [1] [Project]
     Spatial Pyramid Matching [2] [Code]
     Locality-constrained Linear Coding [3] [Project] [Matlab code]
     Sparse Coding [4] [Project] [Matlab code]
     Texture Classification [5] [Project]
     Multiple Kernels for Image Classification [6] [Project]
     Feature Combination [7] [Project]
     SuperParsing [Code]
     Large Scale Correlation Clustering Optimization[Matlab code]
     Detecting and Sketching the Common[Project]
     Self-Tuning Spectral Clustering[Project][Code]
     User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior[Paper][Code]
     Filters for Texture Classification[Project]
     Multiple Kernel Learning for Image Classification[Project]
    SLIC Superpixels[Project]
六、抠图Image Matting
     A Closed Form Solution to Natural Image Matting [Code]
     Spectral Matting [Project]
     Learning-based Matting [Code]
七、目标跟踪Object Tracking：
     A Forest of Sensors - Tracking Adaptive Background Mixture Models [Project]
     Object Tracking via Partial Least Squares Analysis[Paper][Code]
     Robust Object Tracking with Online Multiple Instance Learning[Paper][Code]
     Online Visual Tracking with Histograms and Articulating Blocks[Project]
     Incremental Learning for Robust Visual Tracking[Project]
     Real-time Compressive Tracking[Project]
     Robust Object Tracking via Sparsity-based Collaborative Model[Project]
     Visual Tracking via Adaptive Structural Local Sparse Appearance Model[Project]
     Online Discriminative Object Tracking with Local Sparse Representation[Paper][Code]
     Superpixel Tracking[Project]
     Learning Hierarchical Image Representation with Sparsity, Saliency and Locality[Paper][Code]
     Online Multiple Support Instance Tracking [Paper][Code]
     Visual Tracking with Online Multiple Instance Learning[Project]
     Object detection and recognition[Project]
     Compressive Sensing Resources[Project]
     Robust Real-Time Visual Tracking using Pixel-Wise Posteriors[Project]
     Tracking-Learning-Detection[Project][OpenTLD/C++ Code]
     the HandVu：vision-based hand gesture interface[Project]
八、Kinect：
     Kinect toolbox[Project]
     OpenNI[Project]
     zouxy09 CSDN Blog[Resource]
九、3D相关：
     3D Reconstruction of a Moving Object[Paper] [Code]
     Shape From Shading Using Linear Approximation[Code]
     Combining Shape from Shading and Stereo Depth Maps[Project][Code]
     Shape from Shading: A Survey[Paper][Code]
     A Spatio-Temporal Descriptor based on 3D Gradients (HOG3D)[Project][Code]
     Multi-camera Scene Reconstruction via Graph Cuts[Paper][Code]
     A Fast Marching Formulation of Perspective Shape from Shading under Frontal Illumination[Paper][Code]
     Reconstruction:3D Shape, Illumination, Shading, Reflectance, Texture[Project]
     Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers[Code]
     Learning 3-D Scene Structure from a Single Still Image[Project]
十、机器学习算法：
     Matlab class for computing Approximate Nearest Nieghbor (ANN) [Matlab class providing interface toANN library]
     Random Sampling[code]
     Probabilistic Latent Semantic Analysis (pLSA)[Code]
     FASTANN and FASTCLUSTER for approximate k-means (AKM)[Project]
     Fast Intersection / Additive Kernel SVMs[Project]
     SVM[Code]
     Ensemble learning[Project]
     Deep Learning[Net]
     Deep Learning Methods for Vision[Project]
     Neural Network for Recognition of Handwritten Digits[Project]
     Training a deep autoencoder or a classifier on MNIST digits[Project]
    THE MNIST DATABASE of handwritten digits[Project]
    Ersatz：deep neural networks in the cloud[Project]
    Deep Learning [Project]
    sparseLM : Sparse Levenberg-Marquardt nonlinear least squares in C/C++[Project]
    Weka 3: Data Mining Software in Java[Project]
    Invited talk "A Tutorial on Deep Learning" by Dr. Kai Yu (余凯)[Video]
    CNN - Convolutional neural network class[Matlab Tool]
    Yann LeCun's Publications[Wedsite]
    LeNet-5, convolutional neural networks[Project]
    Training a deep autoencoder or a classifier on MNIST digits[Project]
    Deep Learning 大牛Geoffrey E. Hinton's HomePage[Website]
十一、目标、行为识别Object, Action Recognition：
     Action Recognition by Dense Trajectories[Project][Code]
     Action Recognition Using a Distributed Representation of Pose and Appearance[Project]
     Recognition Using Regions[Paper][Code]
     2D Articulated Human Pose Estimation[Project]
     Fast Human Pose Estimation Using Appearance and Motion via Multi-Dimensional Boosting Regression[Paper][Code]
     Estimating Human Pose from Occluded Images[Paper][Code]
     Quasi-dense wide baseline matching[Project]
     ChaLearn Gesture Challenge: Principal motion: PCA-based reconstruction of motion histograms[Prpject]
十二、图像处理：
     Distance Transforms of Sampled Functions[Project]
    The Computer Vision Homepage[Project]
十三、一些实用工具：
     EGT: a Toolbox for Multiple View Geometry and Visual Servoing[Project] [Code]
     a development kit of matlab mex functions for OpenCV library[Project]
     Fast Artificial Neural Network Library[Project]  

十四、人手及指尖检测与识别：

·           finger-detection-and-gesture-recognition [Code]

·           Hand and Finger Detection using JavaCV[Project]

·           Hand and fingers detection[Code]

十五、场景解释：

·           Nonparametric Scene Parsing via Label Transfer [Project]

十六、光流Optical flow：

·         High accuracy optical flow using a theory for warping [Project]

·         Dense Trajectories Video Description [Project]

·         SIFT Flow: Dense Correspondence across Scenes and its Applications[Project]

·         KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker [Project]

·         Tracking Cars Using Optical Flow[Project]

·         Secrets of optical flow estimation and their principles[Project]

·         implmentation of the Black and Anandan dense optical flow method[Project]

·         Optical Flow Computation[Project]

·         Beyond Pixels: Exploring New Representations and Applications for Motion Analysis[Project]

·         A Database and Evaluation Methodology for Optical Flow[Project]

·         optical flow relative[Project]

·         Robust Optical Flow Estimation [Project]

·         optical flow[Project]

十七、图像检索Image Retrieval：

·           Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval [Paper][code]

十八、马尔科夫随机场Markov Random Fields：

·         Markov Random Fields for Super-Resolution [Project]

·         A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors [Project]

十九、运动检测Motion detection：

·         Moving Object Extraction, Using Models or Analysis of Regions [Project]

·         Background Subtraction: Experiments and Improvements for ViBe [Project]

·         A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications [Project]

·         changedetection.net: A new change detection benchmark dataset[Project]

·         ViBe - a powerful technique for background detection and subtraction in video sequences[Project]

·         Background Subtraction Program[Project]

·         Motion Detection Algorithms[Project]

·         Stuttgart Artificial Background Subtraction Dataset[Project]

·         Object Detection, Motion Estimation, and Tracking[Project]

 

人工智能与模式识别国际顶级期刊会议目录（包含该领域最权威的期刊和会议） 

以上内容汇总与以下网址（贴出网址以表感谢，以示尊重）： 

http://www.sigvc.org/bbs/thread-72-1-1.html 

https://blog.csdn.net/zouxy09/article/details/8550952 

http://blog.sina.com.cn/s/blog_6833a4df01012bcf.html