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计算机视觉牛人博客和代码汇总（全）-转载

2024-10-29 19:07:39   207人阅读

 

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）

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
David Donoho at Stanford Univ.
 
 
大神们：
 
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.
 
 
 
 
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.

 
 
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
 
2 个人、研究机构链接

（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]

  • 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]

  • 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]

  • Ef?cient 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]

  • 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]

  • 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]

  • Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities[Project]


八、Kinect：

  • Kinect toolbox[Project]

  • OpenNI[Project]

  • zouxy09 CSDN Blog[Resource]

  • FingerTracker 手指跟踪[code]


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]

  • 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 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[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：

  • 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]

     

    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.

    • LIOP descriptor – Source code for the Local Intensity order Pattern (LIOP) descriptor (ICCV 2011).

    • 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:

    • GIST – Matlab code for the GIST descriptor

    • CENTRIST – Global visual descriptor for scene categorization and object detection (PAMI 2011)

     

    Feature Coding and Pooling

    • 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.

    • Spatial Pyramid Matching – Source code for feature pooling based on spatial pyramid matching (widely used for image classification)

     

    Convolutional Nets and Deep Learning

    • EBLearn – C++ Library for Energy-Based Learning. It includes several demos and step-by-step instructions to train classifiers based on convolutional neural networks.

    • Torch7 – Provides a matlab-like environment for state-of-the-art machine learning algorithms, including a fast implementation of convolutional neural networks.

    • Deep Learning - Various links for deep learning software.

     

    Part-Based Models

     

    • Deformable Part-based Detector – Library provided by the authors of the original paper (state-of-the-art in PASCAL VOC detection task)

    • Efficient Deformable Part-Based Detector – Branch-and-Bound implementation for a deformable part-based detector.

    • 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).

    • Coarse-to-Fine Deformable Part Model – Fast approach for deformable object detection (CVPR 2011).

    • Poselets – C++ and Matlab versions for object detection based on poselets.

    • 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

      • Relative Attributes – Modified implementation of RankSVM to train Relative Attributes (ICCV 2011).

      • Object Bank – Implementation of object bank semantic features (NIPS 2010). See also ActionBank

      • Classemes, Picodes, and Meta-class features – Software for extracting high-level image descriptors (ECCV 2010, NIPS 2011, CVPR 2012).

      Large-Scale Learning

      • Additive Kernels – Source code for fast additive kernel SVM classifiers (PAMI 2013).

      • LIBLINEAR – Library for large-scale linear SVM classification.

      • VLFeat – Implementation for Pegasos SVM and Homogeneous Kernel map.

      Fast Indexing and Image Retrieval

      • FLANN – Library for performing fast approximate nearest neighbor.

      • Kernelized LSH – Source code for Kernelized Locality-Sensitive Hashing (ICCV 2009).

      • ITQ Binary codes – Code for generation of small binary codes using Iterative Quantization and other baselines such as Locality-Sensitive-Hashing (CVPR 2011).

      • INRIA Image Retrieval – Efficient code for state-of-the-art large-scale image retrieval (CVPR 2011).

      Object Detection

      • See Part-based Models and Convolutional Nets above.

      • Pedestrian Detection at 100fps – Very fast and accurate pedestrian detector (CVPR 2012).

      • Caltech Pedestrian Detection Benchmark – Excellent resource for pedestrian detection, with various links for state-of-the-art implementations.

      • OpenCV – Enhanced implementation of Viola&Jones real-time object detector, with trained models for face detection.

      • Efficient Subwindow Search – Source code for branch-and-bound optimization for efficient object localization (CVPR 2008).

      3D Recognition

      • Point-Cloud Library – Library for 3D image and point cloud processing.

      Action Recognition

      • ActionBank – Source code for action recognition based on the ActionBank representation (CVPR 2012).

      • STIP Features – software for computing space-time interest point descriptors

      • Independent Subspace Analysis – Look for Stacked ISA for Videos (CVPR 2011)

      • Velocity Histories of Tracked Keypoints - C++ code for activity recognition using the velocity histories of tracked keypoints (ICCV 2009)

      Datasets

      Attributes

      • Animals with Attributes – 30,475 images of 50 animals classes with 6 pre-extracted feature representations for each image.

      • aYahoo and aPascal – Attribute annotations for images collected from Yahoo and Pascal VOC 2008.

      • FaceTracer – 15,000 faces annotated with 10 attributes and fiducial points.

      • PubFig – 58,797 face images of 200 people with 73 attribute classifier outputs.

      • [url=http://vis-www.cs.umass.edu/lfw/]LFW[/url] – 13,233 face images of 5,749 people with 73 attribute classifier outputs.

      • Human Attributes – 8,000 people with annotated attributes. Check also this link for another dataset of human attributes.

      • SUN Attribute Database – Large-scale scene attribute database with a taxonomy of 102 attributes.

      • ImageNet Attributes – Variety of attribute labels for the ImageNet dataset.

      • Relative attributes – Data for OSR and a subset of PubFig datasets. Check also this link for the WhittleSearch data.

      • Attribute Discovery Dataset – Images of shopping categories associated with textual descriptions.

      Fine-grained Visual Categorization

      • Caltech-UCSD Birds Dataset – Hundreds of bird categories with annotated parts and attributes.

      • Stanford Dogs Dataset – 20,000 images of 120 breeds of dogs from around the world.

      • Oxford-IIIT Pet Dataset – 37 category pet dataset with roughly 200 images for each class. Pixel level trimap segmentation is included.

      • Leeds Butterfly Dataset – 832 images of 10 species of butterflies.

      • Oxford Flower Dataset – Hundreds of flower categories.

      Face Detection

      • [url=http://vis-www.cs.umass.edu/fddb/]FDDB[/url] – UMass face detection dataset and benchmark (5,000+ faces)

      • CMU/MIT – Classical face detection dataset.

      Face Recognition

      • Face Recognition Homepage – Large collection of face recognition datasets.

      • [url=http://vis-www.cs.umass.edu/lfw/]LFW[/url] – UMass unconstrained face recognition dataset (13,000+ face images).

      • NIST Face Homepage – includes face recognition grand challenge (FRGC), vendor tests (FRVT) and others.

      • CMU Multi-PIE – contains more than 750,000 images of 337 people, with 15 different views and 19 lighting conditions.

      • FERET – Classical face recognition dataset.

      • 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.

      • SCFace – Low-resolution face dataset captured from surveillance cameras.

      Handwritten Digits

      • MNIST – large dataset containing a training set of 60,000 examples, and a test set of 10,000 examples.

      Pedestrian Detection

      • Caltech Pedestrian Detection Benchmark – 10 hours of video taken from a vehicle,350K bounding boxes for about 2.3K unique pedestrians.

      • INRIA Person Dataset – Currently one of the most popular pedestrian detection datasets.

      • ETH Pedestrian Dataset – Urban dataset captured from a stereo rig mounted on a stroller.

      • TUD-Brussels Pedestrian Dataset – Dataset with image pairs recorded in an crowded urban setting with an onboard camera.

      • PASCAL Human Detection – One of 20 categories in PASCAL VOC detection challenges.

      • USC Pedestrian Dataset – Small dataset captured from surveillance cameras.

      Generic Object Recognition

      • ImageNet – Currently the largest visual recognition dataset in terms of number of categories and images.

      • Tiny Images – 80 million 32x32 low resolution images.

      • Pascal VOC – One of the most influential visual recognition datasets.

      • Caltech 101 / Caltech 256 – Popular image datasets containing 101 and 256 object categories, respectively.

      • MIT LabelMe – Online annotation tool for building computer vision databases.

      Scene Recognition

      • MIT SUN Dataset – MIT scene understanding dataset.

      • UIUC Fifteen Scene Categories – Dataset of 15 natural scene categories.

      Feature Detection and Description

      • VGG Affine Dataset – Widely used dataset for measuring performance of feature detection and description. CheckVLBenchmarksfor an evaluation framework.

      Action Recognition

      • Benchmarking Activity Recognition – CVPR 2012 tutorial covering various datasets for action recognition.

      RGBD Recognition

      • 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)

      • LBP特征(一种纹理特征)：http://www.comp.hkbu.edu.hk/~icpr06/tutorials/Pietikainen.html

      • Fast Corner Detection（OpenCV中的Fast算法）:FAST Corner Detection -- Edward Rosten

      机器视觉

      • 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

      • 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

      • Adaboost Matlab 工具：http://graphics.cs.msu.ru/en/science/research/machinelearning/adaboosttoolbox

      • MultiBoost(不说啥了，多类Adaboost算法的程序)：http://sourceforge.net/projects/multiboost/

      • TextonBoost(我们教研室王冠夫师兄的毕设): Jamie Shotton - Code

      • LibSvm的老爹（推荐）: http://www.csie.ntu.edu.tw/~cjlin/

      • Conditional Random Fields（CRF论文+Code列表，推荐）

      • CRF++: Yet Another CRF toolkit

      • Conditional Random Field (CRF) Toolbox for Matlab

      • Tree CRFs

      • LingPipe: Installation

      • Hidden Markov Models（推荐）

      • 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）

      综合代码

      • 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/

      • 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

      • 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

      • 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/

      • 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

      • 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/

      • Evolutive Algorithm based on Na?ve Bayes models Estimation（单独列了一个算法的Code）：http://www.cvc.uab.cat/~xbaro/eanbe/#_Software

      主页代码

      • Pablo Negri‘s Home Page

      • Jianxin Wu‘s homepage

      • Peter Carbonetto

      • Markov Random Fields for Super-Resolution

      • Detecting and Sketching the Common

      • Pedro Felzenszwalb

      • Hae JONG, SEO

      • CAP 5416 - Computer Vision

      • Parallel Tracking and Mapping for Small AR Workspaces (PTAM)

      • Deva Ramanan - UC Irvine - Computer Vision

      • Raghuraman Gopalan

      • Hui Kong

      • Jamie Shotton - Post-Doctoral Researcher in Computer Vision

      • Jean-Yves AUDIBERT

      • Olga Veksler

      • Stephen Gould

      • Publications (Last Update: 09/30/10)

      • Karim Ali - FlowBoost

      • A simple parts and structure object detector

      • Code - Oxford Brookes Vision Group

      • Taku Kudo

      行人检测

      • Histogram of Oriented Gradient (Windows)

      • INRIA Pedestrian detector

      • Poselets

      • William Robson Schwartz - Softwares

      • calvin upper-body detector v1.02

      • RPT@CVG

      • Main Page

      • Source Code

      • Dr. Luciano Spinello

      • Pedestrian Detection

      • Class-Specific Hough Forests for Object Detection

      • Jianxin Wu‘s homepage（就是上面的）

      • 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/

      视觉壁障

      • High Speed Obstacle Avoidance using Monocular Vision and Reinforcement Learning

      • TLD(2010年很火的tracking算法)

      • online boosting trackers

      • Boris Babenko

      • 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/

      物体检测算法

      • Object Detection

      • Software for object detection

      人脸检测

      • Source Code

      • 10个人脸检测项目

      • Jianxin Wu‘s homepage（又是这货）

      ICA独立成分分析

      • An ICA page-papers,code,demo,links (Tony Bell)

      • FastICA

      • Cached k-d tree search for ICP algorithms

      滤波算法

      • 卡尔曼滤波：The Kalman Filter(终极网页)

      • Bayesian Filtering Library: The Bayesian Filtering Library

      路面识别

      • Source Code

      • Vanishing point detection for general road detection

      分割算法

      • MATLAB Normalized Cuts Segmentation Code：software

      • 超像素分割：SLIC Superpixels

附： http://blog.sina.com.cn/s/blog_5086c3e20101kdy5.html，http://www.yuanyong.org/cv/cv-code-three.html

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

http://www.sigvc.org/bbs/forum.phpmod=viewthread&tid=3126&highlight=%BC%C6%CB%E3%BB%FA%CA%D3%BE%F5%B4%FA%C2%EB

转载自：

http://www.cnblogs.com/findumars/p/5009003.html

 

 

计算机视觉牛人博客和代码汇总（全）-转载


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