每个做过或者正在做研究工作的人都会关注一些自己认为有价值的、活跃的研究组和个人的主页，关注他们的主页有时候比盲目的去搜索一些论文有用多了，大牛的或者活跃的研究者主页往往提供了他们的最新研究线索，顺便还可八一下各位大牛的经历，对于我这样的小菜鸟来说最最实惠的是有时可以找到源码，很多时候光看论文是理不清思路的。
1 牛人Homepages（随意排序，不分先后）：
1.USC Computer Vision Group：南加大，多目标跟踪/检测等；
2.ETHZ Computer Vision Laboratory：苏黎世联邦理工学院，欧洲最好的几个CV/ML研究机构；
3.Helmut Grabner：Online Boosting and Vision的作者，tracking by online feature selection的早期经典，貌似现在不是很活跃了，跑去创业了；
4.Robert T. Collins：PSU，也是跟踪界的大牛；
5.Ying Wu：美国西北大学，华人学者中的翘楚；
6.Junsong Yuan：NTU，上面Wu老师的学生；
7.James W. Davis：俄亥俄州立，视频监控；
8. The Australian Centre for Visual Technologies：阿德莱德大学的CV组，最近也是exceedingly active & fruitful；
9.Chunhua Shen：属上面的ACVT组，最近非常活跃；
10.Xi Li：同属ACVT，之前是中科院的PHD，跟踪方面的论文很多，有理论深度；
11.Haibin Ling：天普大学，L1-Tracker及后续扩展，源码分享；
12.Learning, Recognition, and Surveillance：奥地利 TU Graz，在线学习，跟踪/检测等，active！源码分享；
13.Statistical Visual Computing Laboratory：UCSD，光听名字就很学术吧，Saliency研究很有名；
14.David Ross：多伦多大学，IVT的作者，跟踪中Generative表观的经典中的经典，提供源码，IVT的代码结构被后来很多人引用，值得一读；
15.EPFL, Computer Vision Laboratory：洛桑理工的学院，和上面的的ETHZ CV lab同样是欧洲最好的CV研究大组；
16.Jamie Shotton：属微软剑桥研究中心，Decision/Regression Forests；
17.Sinisa Todorovic：俄勒冈州立，行为分析等；
18.Shi Jianbo：大名鼎鼎的Good Feature to Track作者，目前方向行为分析和多目标跟踪等；
19.Shai Avidan：特拉维夫大学，大牛级，可算是Tracking-by-detection的开创者，Ensemble Tracking, SVM Tracking；
20.Visual Information Processing and Learning：中科院计算所，山世光老师的研究组，不需介绍了吧；
21.Shaogang Gong：Queen Mary University of London，各种PAMI，IJCV；
22.Yang Jian：南京理工大学，2DPCA，人脸识别；
23.CALVIN：weakly supervised learning，objectness；
24.Learning & Vision Group：NUS，稀疏表示；
26.Xiaogang Wang：CUHK，active & fruitful，行人检测，群体行为分析；
27.Zhou, Bolei：上面Wang老师硕士研究生，群体行为，看看人家的Publications已经轻松甩国内博士好几条街；
28.Computational Vision Group：Leader--Deva Ramanan；
29.Zhang Lei：香港理工，稀疏表示，人脸识别，可以算大中华区比较活跃的研究组了，几乎每篇论文都有对应源码；
30.Zhang Kaihua：上面Zhang老师学生，Compressive Tracking；
31.Pramod Sharma：离线训练检测器的在线自适应，貌似是个不错的topic；
32.Loris Bazzani：person re-id，他的SDALF(code)描述子经常被用来做为比较对象，说明还是有参考价值的；
33.Pedro Felzenszwalb：布朗大学，目标检测，新新N人一枚；
34.Vijayakumar Bhagavatula：IEEE Fellow， correlation filters；
35.Laurens van der Maaten：MLer.
牛人主页（主页有很多论文代码）
（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/%E9%A6%96%E9%A1%B5
（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/ 图像分解，检索
(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%20CH.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)南京大学教授吴建鑫：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)图片检索国际竞赛PASCAL 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
(109)数据堂-图像处理相关的样本数据：http://www.datatang.com/data/list/602026/p1
(110)东软基于CV的汽车辅助驾驶系统：http://www.neusoft.com/cn/solutions/1047/
(111)马里兰大学教授Rema Chellappa:http://www.cfar.umd.edu/~rama/
(112)芝加哥丰田研究中心助理教授Devi Parikh：http://ttic.uchicago.edu/~dparikh/index.html
(113)宾夕法尼亚大学助理教授石建波：http://www.cis.upenn.edu/~jshi/
(114)比利时鲁汶大学教授Luc Van Gool：http://www.vision.ee.ethz.ch/members/get_member.cgi?id=1, http://www.vision.ee.ethz.ch/~vangool/
(115)行人检测主页：http://www.pedestrian-detection.com/
(116)法国学习算法与系统实验室Basilio Noris博士：http://lasa.epfl.ch/people/member.php?SCIPER=129576 http://mldemos.epfl.ch/
(117)美国马里兰大学LARRY S.DAVIS教授：http://www.umiacs.umd.edu/~lsd/
(118)计算机视觉论文分类导航：http://www.visionbib.com/bibliography/contents.html
(119)计算机视觉分类信息导航：http://www.visionbib.com/
(120)西班牙马德里理工大学博士Marcos Nieto：http://marcosnieto.net/
(121)香港理工大学副教授张磊：http://www4.comp.polyu.edu.hk/~cslzhang/
(122)以色列技术学院教授Michael Elad：http://www.cs.technion.ac.il/~elad/
(123)韩国启明大学计算机视觉与模式识别实验室：http://cvpr.kmu.ac.kr/
(124)英国诺丁汉大学Michel Valstar博士：http://www.cs.nott.ac.uk/~mfv/
(125)卡内基梅隆大学Takeo Kanade教授:http://www.ri.cmu.edu/people/kanade_takeo.html
(126)微软学术搜索：http://libra.msra.cn/
(127)比利时天主教鲁汶大学Radu Timofte博士：http://homes.esat.kuleuven.be/~rtimofte/，交通标志检测，定位，3D跟踪
(128)迪斯尼匹兹堡研究院研究员：Iain Matthews:http://www.iainm.com/iainm/Home.html
http://www.ri.cmu.edu/person.html?type=publications&person_id=741 AAM,三维重建
（129）康奈尔大学视觉与图像分析组：http://www.via.cornell.edu/ 医学图像处理
（130）密西根州立大学生物识别研究组：http://www.cse.msu.edu/biometrics/ 人脸识别、指纹识别、图像检索
（131）柏林科技大学计算机视觉与遥感实验室：http://www.cv.tu-berlin.de/menue/computer_vision_remote_sensing/parameter/en/ 图像分析、物体重建、基于图像的表面测量、医学图像处理
（132）英国布里斯托大学数字多媒体研究组：http://www.cs.bris.ac.uk/Research/Digitalmedia/ 运动检测与跟踪、视频压缩、3D重建、字符定位
（133）英国萨利大学视觉、语音与信号处理中心： http://www.surrey.ac.uk/cvssp/ 人脸识别、监控、3D、视频检索、
（134）北卡莱罗纳大学教堂山分校Marc Pollefeys教授：http://www.cs.unc.edu/~marc/ 基于视频的3D模型生成、相机标定、运动检测与分析、3D重建
（135）澳大利亚国立大学Richard Hartley教授：http://users.cecs.anu.edu.au/~hartley/ 运动估计、稀疏子空间、跟踪、
（136）百度技术副总监于凯：http://www.dbs.ifi.lmu.de/~yu_k/ 深度学习，稀疏表示，图像分类
（137）西安电子科技大学高新波教授：http://web.xidian.edu.cn/xbgao/index.html 质量评判、水印、稀疏表示、超分辨率
（138）加州大学伯克利分校Michael I.Jordan教授：http://www.cs.berkeley.edu/~jordan/ 机器学习
（139）加州理工行人检测相关资料：http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/
（140）微软Redmond研究院研究员Piotr Dollar: http://vision.ucsd.edu/~pdollar/ 行人检测、特征提取、
（141）视觉计算研究论坛：http://www.sigvc.org/bbs/ 中科院视觉计算研究小组的论坛
（142）美国坦桑尼亚州立大学稀疏学习软件包：http://www.public.asu.edu/~jye02/Software/SLEP/index.htm 稀疏学习
（143）美国加州大学圣地亚哥分校Jacob Whitehill博士：http://mplab.ucsd.edu/~jake/ 机器学习
（144）美国布朗大学Michael J.Black教授：http://cs.brown.edu/~black/ 人的姿态估计和跟踪
（145）美国加州大学圣地亚哥分校David Kriegman教授：http://cseweb.ucsd.edu/~kriegman/ 人脸识别
（146）南加州大学Paul Debevec教授：http://ict.debevec.org/~debevec/ 或 http://www.pauldebevec.com/ 将CV和CG结合研究 人脸捕捉重建技术
（147）伊利诺伊大学D.A.Forsyth教授：http://luthuli.cs.uiuc.edu/~daf/ 三维重建
（148）英国牛津大学Ian Reid教授：http://www.robots.ox.ac.uk/~ian/ 跟踪和机器人导航
（149）CMU大学Alyosha Efros 教授: https://www.cs.cmu.edu/~efros/ 图像纹理合成
（150）加州大学伯克利分校Jitendra Malik教授：http://www.cs.berkeley.edu/~malik/ 轮廓检测、图像/视频分割、图形匹配、目标识别
（151）MIT教授William Freeman： http://people.csail.mit.edu/billf/ 图像纹理合成
（152）CMU博士Henry Schneiderman： http://www.cs.cmu.edu/~hws/ 目标检测和识别；
（153）微软研究员Paul Viola: http://research.microsoft.com/en-us/um/people/viola/ AdaBoost算法
（154）微软研究员Antonio Criminisi: http://research.microsoft.com/en-us/people/antcrim/ 图像修补，三维重建，目标检测与跟踪；
（155）魏茨曼科学研究所教授Michal Irani: http://www.wisdom.weizmann.ac.il/~irani/ 超分辨率
（156）瑞士洛桑理工学院Pascal Fua教授：http://people.epfl.ch/pascal.fua/bio?lang=en 立体视觉，增强现实
（157）佐治亚理工学院Irfan Essa教授：http://www.ic.gatech.edu/people/irfan-essa 人脸表情识别
（158）中科院助理教授樊彬：http://www.sigvc.org/bfan/ 特征描述；
（159）斯坦福大学Sebastian Thrun教授：http://robots.stanford.edu/index.html 机器人；
（160）多伦多大学Geoffrey E.Hinton教授：http://www.cs.toronto.edu/~hinton/ 深度学习
（161）凤巢系统架构师张栋博士：http://weibo.com/machinelearning
（162）2012年龙星计划机器学习课程：http://bigeye.au.tsinghua.edu.cn/DragonStar2012/index.html
（163）中科院自动化所肖柏华教授：http://www.compsys.ia.ac.cn/people/xiaobaihua.html 文字识别、人脸识别、质量评判
（164）图像视频质量评判：http://live.ece.utexas.edu/research/quality/
（165）纽约大学Yann LeCun教授http://yann.lecun.com/ http://yann.lecun.com/exdb/mnist/ 手写体数字识别
（166）二维条码识别开源库zxing：http://code.google.com/p/zxing/
（167）布朗大学Pedro Felzenszwalb教授：http://cs.brown.edu/~pff/ 特征提取，Deformable Part Model
（168）伊利诺伊香槟大学Svetlana Lazebnik教授：http://www.cs.illinois.edu/homes/slazebni/ 特征提取，聚类，图像检索
（169）荷兰乌德勒支大学图像与多媒体研究中心http://www.cs.uu.nl/centers/give/multimedia/index.html 图像、多媒体检索与匹配
（170）英国格拉斯哥大学信息检索小组：http://ir.dcs.gla.ac.uk/ 文本、图像、视频检索
（171）中科院自动化所孙哲南助理教书：http://www.cbsr.ia.ac.cn/users/znsun/ 虹膜识别、掌纹识别、人脸识别
（172）南京信息工程大学刘青山教授：http://www.jstuoke.com/web/xky/detail.asp?NewsID=1096 人脸图像分析、医学图像分析
（173）清华大学助理教授冯建江：http://ivg.au.tsinghua.edu.cn/~jfeng/ 指纹识别
（174）北航助理教授黄迪：http://irip.buaa.edu.cn/~dihuang/ 3D人脸识别
（175）中山大学助理教授郑伟诗：http://sist.sysu.edu.cn/~zhwshi/ 人脸识别、特征匹配、聚类、检索；
（176）google瑞士苏黎世的工程师Thomas Deselaers: http://thomas.deselaers.de/index.html 图像检索
（177）百度深度学习研究中心博士后余轶南：http://www.cbsr.ia.ac.cn/users/ynyu/index.htm 目标检测，图像检索
（178）威兹曼科技大学超分辨率：http://www.wisdom.weizmann.ac.il/~vision/SingleImageSR.html
（179）德克萨斯大学奥斯汀分校Al Bovik教授：http://live.ece.utexas.edu/people/bovik/ 图像视频质量判别、特征提取
（180）以色列希伯来大学Yair Weiss教授：http://www.cs.huji.ac.il/~yweiss/ 机器学习、超分辨率
（181）以色列希伯来大学Daniel Zoran博士：http://www.cs.huji.ac.il/~daniez/ 超分辨率、去噪
（182）美国加州大学Peyman Milanfar教授：http://users.soe.ucsc.edu/~milanfar/ 去噪
（183）中科院计算所副研究员常虹：http://www.jdl.ac.cn/user/hchang/index.html 图像检索、半监督学习、超分辨率
（184）以色列威茨曼大学Anat Levin教授：http://www.wisdom.weizmann.ac.il/~levina/ 去噪、去模糊
（185）以色列威茨曼大学Daniel Glasner博士后：http://www.wisdom.weizmann.ac.il/~glasner/ 超分辨率、分割、姿态估计
（186）密西根大学助理教授Honglak Lee: http://web.eecs.umich.edu/~honglak/ 机器学习、特征提取，去噪、稀疏表示；
（187）MIT周博磊博士：http://people.csail.mit.edu/bzhou/ 聚集分析、运动检测
（188）美国田纳西大学Li He博士：http://web.eecs.utk.edu/~lhe4/ 稀疏表示、超分辨率；
（189）Adobe研究院Jianchao Yang研究员：http://www.ifp.illinois.edu/~jyang29/ 稀疏表示，超分辨率、图片检索、去噪、去模糊
（190）Deep Learning主页：http://deeplearning.net/ 深度学习论文、软件，代码，demo，数据等；
（191）斯坦福大学Andrew Ng教授：http://cs.stanford.edu/people/ang/ 深度神经网络，深度学习
（192）Elefant: http://elefant.developer.nicta.com.au/ 机器学习开源库
（193）微软研究员Ce Liu: http://people.csail.mit.edu/celiu/ 去噪、超分辨率、去模糊、分割
（194）West Virginia大学助理教授Xin Li: http://www.csee.wvu.edu/~xinl/ 边缘检测、降噪、去模糊
（195）http://www.csee.wvu.edu/~xinl/source.html 深度学习、去噪、编码、压缩感知、超分辨率、聚类、分割等相关代码集合
（196）西班牙格拉纳达大学超分辨率重建项目组：http://decsai.ugr.es/pi/superresolution/index.html
（197）清华大学程明明博士：http://mmcheng.net/ 图像分割、检索
（198）牛津布鲁克斯大学Philip H.S.Torr教授：http://cms.brookes.ac.uk/staff/PhilipTorr/ 分割、三维重建
（199）佐治亚理工学院James M.Rehg教授：http://www.cc.gatech.edu/~rehg/ 分割、行人检测、特征描述、
（200）大规模图像分类、检测竞赛ILSVRC（Stanford, Google举办）:
http://www.image-net.org/challenges/LSVRC/2013/
（201）加州大学尔湾分校Deva Ramanan助理教授：http://www.ics.uci.edu/~dramanan/ 目标检测，行人检测，跟踪、稀疏表示
（202）人脸识别测试图片集：http://www.mlcv.net/
（203）美国西北大学博士Ming Yang: http://www.ece.northwestern.edu/~mya671/ 人脸识别、图像检索；
（204）美国加州大学伯克利分校博士后Ross B.Girshick：http://www.cs.berkeley.edu/~rbg/ 目标检测（DPM）
（205）中文语言资源联盟：http://www.chineseldc.org/index.html 内有很多语言识别、字符识别的训练，测试库；
（206）西班牙巴塞罗那大学计算机视觉中心：http://www.cvc.uab.es/adas/site/ 检测、跟踪、3D、行人检测、汽车辅助驾驶
（207）德国戴姆勒研究所Prof. Dr. Dariu M. Gavrila：http://www.gavrila.net/index.html 跟踪、行人检测、
（208）苏黎世联邦理工学院Andreas Ess博士后：http://www.vision.ee.ethz.ch/~aess/ 行人检测、行为检测、跟踪
（209）Libqrencode: http://fukuchi.org/works/qrencode/ 基于C语言的QR二维码编码开源库
（210）江西财经大学袁飞牛教授：http://sit.jxufe.cn/grbk/yfn/index.html# 烟雾检测、3D重建、医学图像处理
（211）耶路撒冷大学Raanan Fattal教师：http://www.cs.huji.ac.il/~raananf/ 图像增强、
（212）耶路撒冷大学Dani Lischnski教授：http://www.cs.huji.ac.il/~danix/ 去模糊、纹理合成、图像增强
3 代码汇总
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]
-
Boundary Preserving Dense Local Regions [15][Project]
-
Weighted Histogram[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]
-
Real-time Facial Feature Detection using Conditional Regression Forests[Project]
-
Global and Efficient Self-Similarity for Object Classification and Detection[code]
-
WαSH: Weighted α-Shapes for Local Feature Detection[Project]
-
HOG[Project]
-
Online Selection of Discriminative Tracking Features[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]
-
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]
-
A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction[Paper][Code]
-
Level Set Method Research by Chunming Li[Project]
-
ClassCut for Unsupervised Class Segmentation[code]
-
SEEDS: Superpixels Extracted via Energy-Driven Sampling [Project][other]
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]
-
Scalable Multi-class Object Detection[Project]
-
Class-Specific Hough Forests for Object Detection[Project]
-
Deformed Lattice Detection In Real-World Images[Project]
-
Discriminatively trained deformable part models[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]
-
Saliency Detection: A Spectral Residual Approach[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]
-
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]
-
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]
-
Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities[Project]
八、Kinect：
九、3D相关：
-
Shape From Shading Using Linear Approximation[Code]
-
Combining Shape from Shading and Stereo Depth Maps[Project][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]
-
Multiple Instance Logistic Discriminant-based Metric Learning (MildML) and Logistic Discriminant-based Metric Learning (LDML)[Code]
-
Sparse coding simulation software[Project]
-
Visual Recognition and Machine Learning Summer School[Software]
十一、目标、行为识别Object, Action Recognition：
-
Action Recognition Using a Distributed Representation of Pose and Appearance[Project]
-
2D Articulated Human Pose Estimation[Project]
-
Fast Human Pose Estimation Using Appearance and Motion via Multi-Dimensional Boosting Regression[Paper][Code]
-
Quasi-dense wide baseline matching[Project]
-
ChaLearn Gesture Challenge: Principal motion: PCA-based reconstruction of motion histograms[Project]
-
Real Time Head Pose Estimation with Random Regression Forests[Project]
-
2D Action Recognition Serves 3D Human Pose Estimation[
-
A Hough Transform-Based Voting Framework for Action Recognition[
-
Motion Interchange Patterns for Action Recognition in Unconstrained Videos[
-
2D articulated human pose estimation software[Project]
-
Learning and detecting shape models [code]
-
Progressive Search Space Reduction for Human Pose Estimation[Project]
-
Learning Non-Rigid 3D Shape from 2D Motion[Project]
十二、图像处理：
-
Distance Transforms of Sampled Functions[Project]
-
The Computer Vision Homepage[Project]
-
Efficient appearance distances between windows[code]
-
Image Exploration algorithm[code]
-
Motion Magnification 运动放大 [Project]
-
Bilateral Filtering for Gray and Color Images 双边滤波器 [Project]
-
A Fast Approximation of the Bilateral Filter using a Signal Processing Approach [
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：
十八、马尔科夫随机场Markov Random Fields：
-
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]
Feature Detection and Description
General Libraries:
-
VLFeat – Implementation of various feature descriptors (including SIFT, HOG, and LBP) and covariant feature detectors (including DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris). Easy-to-use Matlab interface. See Modern features: Software – Slides providing a demonstration of VLFeat and also links to other software. Check also VLFeat hands-on session training
-
OpenCV – Various implementations of modern feature detectors and descriptors (SIFT, SURF, FAST, BRIEF, ORB, FREAK, etc.)
Fast Keypoint Detectors for Real-time Applications:
-
FAST – High-speed corner detector implementation for a wide variety of platforms
-
AGAST – Even faster than the FAST corner detector. A multi-scale version of this method is used for the BRISK descriptor (ECCV 2010).
Binary Descriptors for Real-Time Applications:
-
BRIEF – C++ code for a fast and accurate interest point descriptor (not invariant to rotations and scale) (ECCV 2010)
-
ORB – OpenCV implementation of the Oriented-Brief (ORB) descriptor (invariant to rotations, but not scale)
-
BRISK – Efficient Binary descriptor invariant to rotations and scale. It includes a Matlab mex interface. (ICCV 2011)
-
FREAK – Faster than BRISK (invariant to rotations and scale) (CVPR 2012)
SIFT and SURF Implementations:
-
SIFT: VLFeat, OpenCV, Original code by David Lowe, GPU implementation, OpenSIFT
-
SURF: Herbert Bay’s code, OpenCV, GPU-SURF
Other Local Feature Detectors and Descriptors:
-
VGG Affine Covariant features – Oxford code for various affine covariant feature detectors and descriptors.
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LIOP descriptor – Source code for the Local Intensity order Pattern (LIOP) descriptor (ICCV 2011).
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Local Symmetry Features – Source code for matching of local symmetry features under large variations in lighting, age, and rendering style (CVPR 2012).
Global Image Descriptors:
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GIST – Matlab code for the GIST descriptor
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CENTRIST – Global visual descriptor for scene categorization and object detection (PAMI 2011)
Feature Coding and Pooling
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VGG Feature Encoding Toolkit – Source code for various state-of-the-art feature encoding methods – including Standard hard encoding, Kernel codebook encoding, Locality-constrained linear encoding, and Fisher kernel encoding.
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Spatial Pyramid Matching – Source code for feature pooling based on spatial pyramid matching (widely used for image classification)
Convolutional Nets and Deep Learning
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EBLearn – C++ Library for Energy-Based Learning. It includes several demos and step-by-step instructions to train classifiers based on convolutional neural networks.
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Torch7 – Provides a matlab-like environment for state-of-the-art machine learning algorithms, including a fast implementation of convolutional neural networks.
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Deep Learning - Various links for deep learning software.
Part-Based Models
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Deformable Part-based Detector – Library provided by the authors of the original paper (state-of-the-art in PASCAL VOC detection task)
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Efficient Deformable Part-Based Detector – Branch-and-Bound implementation for a deformable part-based detector.
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Accelerated Deformable Part Model – Efficient implementation of a method that achieves the exact same performance of deformable part-based detectors but with significant acceleration (ECCV 2012).
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Coarse-to-Fine Deformable Part Model – Fast approach for deformable object detection (CVPR 2011).
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Poselets – C++ and Matlab versions for object detection based on poselets.
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Part-based Face Detector and Pose Estimation – Implementation of a unified approach for face detection, pose estimation, and landmark localization (CVPR 2012).
Attributes and Semantic Features
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Relative Attributes – Modified implementation of RankSVM to train Relative Attributes (ICCV 2011).
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Object Bank – Implementation of object bank semantic features (NIPS 2010). See also ActionBank
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Classemes, Picodes, and Meta-class features – Software for extracting high-level image descriptors (ECCV 2010, NIPS 2011, CVPR 2012).
Large-Scale Learning
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Additive Kernels – Source code for fast additive kernel SVM classifiers (PAMI 2013).
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LIBLINEAR – Library for large-scale linear SVM classification.
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VLFeat – Implementation for Pegasos SVM and Homogeneous Kernel map.
Fast Indexing and Image Retrieval
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FLANN – Library for performing fast approximate nearest neighbor.
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Kernelized LSH – Source code for Kernelized Locality-Sensitive Hashing (ICCV 2009).
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ITQ Binary codes – Code for generation of small binary codes using Iterative Quantization and other baselines such as Locality-Sensitive-Hashing (CVPR 2011).
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INRIA Image Retrieval – Efficient code for state-of-the-art large-scale image retrieval (CVPR 2011).
Object Detection
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See Part-based Models and Convolutional Nets above.
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Pedestrian Detection at 100fps – Very fast and accurate pedestrian detector (CVPR 2012).
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Caltech Pedestrian Detection Benchmark – Excellent resource for pedestrian detection, with various links for state-of-the-art implementations.
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OpenCV – Enhanced implementation of Viola&Jones real-time object detector, with trained models for face detection.
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Efficient Subwindow Search – Source code for branch-and-bound optimization for efficient object localization (CVPR 2008).
3D Recognition
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Point-Cloud Library – Library for 3D image and point cloud processing.
Action Recognition
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ActionBank – Source code for action recognition based on the ActionBank representation (CVPR 2012).
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STIP Features – software for computing space-time interest point descriptors
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Independent Subspace Analysis – Look for Stacked ISA for Videos (CVPR 2011)
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Velocity Histories of Tracked Keypoints - C++ code for activity recognition using the velocity histories of tracked keypoints (ICCV 2009)
Datasets
Attributes
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Animals with Attributes – 30,475 images of 50 animals classes with 6 pre-extracted feature representations for each image.
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aYahoo and aPascal – Attribute annotations for images collected from Yahoo and Pascal VOC 2008.
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FaceTracer – 15,000 faces annotated with 10 attributes and fiducial points.
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PubFig – 58,797 face images of 200 people with 73 attribute classifier outputs.
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[url=http://vis-www.cs.umass.edu/lfw/]LFW[/url] – 13,233 face images of 5,749 people with 73 attribute classifier outputs.
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Human Attributes – 8,000 people with annotated attributes. Check also this link for another dataset of human attributes.
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SUN Attribute Database – Large-scale scene attribute database with a taxonomy of 102 attributes.
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ImageNet Attributes – Variety of attribute labels for the ImageNet dataset.
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Relative attributes – Data for OSR and a subset of PubFig datasets. Check also this link for the WhittleSearch data.
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Attribute Discovery Dataset – Images of shopping categories associated with textual descriptions.
Fine-grained Visual Categorization
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Caltech-UCSD Birds Dataset – Hundreds of bird categories with annotated parts and attributes.
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Stanford Dogs Dataset – 20,000 images of 120 breeds of dogs from around the world.
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Oxford-IIIT Pet Dataset – 37 category pet dataset with roughly 200 images for each class. Pixel level trimap segmentation is included.
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Leeds Butterfly Dataset – 832 images of 10 species of butterflies.
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Oxford Flower Dataset – Hundreds of flower categories.
Face Detection
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[url=http://vis-www.cs.umass.edu/fddb/]FDDB[/url] – UMass face detection dataset and benchmark (5,000+ faces)
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CMU/MIT – Classical face detection dataset.
Face Recognition
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Face Recognition Homepage – Large collection of face recognition datasets.
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[url=http://vis-www.cs.umass.edu/lfw/]LFW[/url] – UMass unconstrained face recognition dataset (13,000+ face images).
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NIST Face Homepage – includes face recognition grand challenge (FRGC), vendor tests (FRVT) and others.
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CMU Multi-PIE – contains more than 750,000 images of 337 people, with 15 different views and 19 lighting conditions.
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FERET – Classical face recognition dataset.
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Deng Cai’s face dataset in Matlab Format – Easy to use if you want play with simple face datasets including Yale, ORL, PIE, and Extended Yale B.
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SCFace – Low-resolution face dataset captured from surveillance cameras.
Handwritten Digits
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MNIST – large dataset containing a training set of 60,000 examples, and a test set of 10,000 examples.
Pedestrian Detection
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Caltech Pedestrian Detection Benchmark – 10 hours of video taken from a vehicle,350K bounding boxes for about 2.3K unique pedestrians.
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INRIA Person Dataset – Currently one of the most popular pedestrian detection datasets.
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ETH Pedestrian Dataset – Urban dataset captured from a stereo rig mounted on a stroller.
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TUD-Brussels Pedestrian Dataset – Dataset with image pairs recorded in an crowded urban setting with an onboard camera.
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PASCAL Human Detection – One of 20 categories in PASCAL VOC detection challenges.
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USC Pedestrian Dataset – Small dataset captured from surveillance cameras.
Generic Object Recognition
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ImageNet – Currently the largest visual recognition dataset in terms of number of categories and images.
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Tiny Images – 80 million 32x32 low resolution images.
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Pascal VOC – One of the most influential visual recognition datasets.
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Caltech 101 / Caltech 256 – Popular image datasets containing 101 and 256 object categories, respectively.
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MIT LabelMe – Online annotation tool for building computer vision databases.
Scene Recognition
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MIT SUN Dataset – MIT scene understanding dataset.
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UIUC Fifteen Scene Categories – Dataset of 15 natural scene categories.
Feature Detection and Description
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VGG Affine Dataset – Widely used dataset for measuring performance of feature detection and description. CheckVLBenchmarksfor an evaluation framework.
Action Recognition
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Benchmarking Activity Recognition – CVPR 2012 tutorial covering various datasets for action recognition.
RGBD Recognition
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RGB-D Object Dataset – Dataset containing 300 common household objects
Reference:
[1]: http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html
特征提取-
SURF特征: http://www.vision.ee.ethz.ch/software/index.de.html(当然这只是其中之一)
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LBP特征(一种纹理特征)：http://www.comp.hkbu.edu.hk/~icpr06/tutorials/Pietikainen.html
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Fast Corner Detection（OpenCV中的Fast算法）:FAST Corner Detection -- Edward Rosten
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A simple object detector with boosting(Awarded the Best Short Course Prize at ICCV 2005，So了解adaboost的推荐之作)：http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html
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Boosting(该网页上有相当全的Boosting的文章和几个Boosting代码，本人推荐)：http://cbio.mskcc.org/~aarvey/boosting_papers.html
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Adaboost Matlab 工具：http://graphics.cs.msu.ru/en/science/research/machinelearning/adaboosttoolbox
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MultiBoost(不说啥了，多类Adaboost算法的程序)：http://sourceforge.net/projects/multiboost/
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TextonBoost(我们教研室王冠夫师兄的毕设): Jamie Shotton - Code
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Conditional Random Fields（CRF论文+Code列表，推荐）
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隐马尔科夫模型(Hidden Markov Models)系列之一 - eaglex的专栏 - 博客频道 - CSDN.NET（推荐）
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CvPapers(好吧，牛吧网站，里面有ICCV，CVPR，ECCV，SIGGRAPH的论文收录，然后还有一些论文的代码搜集，要求加精！)：http://www.cvpapers.com/
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Computer Vision Software(里面代码很多，并详细的给出了分类)：http://peipa.essex.ac.uk/info/software.html
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某人的Windows Live（我看里面东东不少就收藏了）：https://skydrive.live.com/?cid=3b6244088fd5a769#cid=3B6244088FD5A769&id=3B6244088FD5A769!523
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MATLAB and Octave Functions for Computer Vision and Image Processing（这个里面的东西也很全，只是都是用Matlab和Octave开发的）：http://www.csse.uwa.edu.au/~pk/research/matlabfns/
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Computer Vision Resources（里面的视觉算法很多，给出了相应的论文和Code，挺好的）：https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html
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MATLAB Functions for Multiple View Geometry（关于物体多视角计算的库）：http://www.robots.ox.ac.uk/~vgg/hzbook/code/
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Evolutive Algorithm based on Naïve Bayes models Estimation（单独列了一个算法的Code）：http://www.cvc.uab.cat/~xbaro/eanbe/#_Software
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Jianxin Wu's homepage（就是上面的）
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Berkeley大学做的Pedestrian Detector，使用交叉核的支持向量机，特征使用HOG金字塔，提供Matlab和C++混编的代码：http://www.cs.berkeley.edu/~smaji/projects/ped-detector/
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High Speed Obstacle Avoidance using Monocular Vision and Reinforcement Learning
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TLD(2010年很火的tracking算法)
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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/
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卡尔曼滤波：The Kalman Filter(终极网页)
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Bayesian Filtering Library: The Bayesian Filtering Library
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MATLAB Normalized Cuts Segmentation Code：software
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超像素分割：SLIC Superpixels
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参考：
http://blog.csdn.net/carson2005/article/details/6601109
http://blog.csdn.net/chlele0105/article/details/16880049
http://blog.csdn.net/yihaizhiyan/article/details/6583727
转载自：