微机视觉牛人博客及代码汇总(全)

每个做过或者正在举行研究工作之人头都见面关注有投机认为产生价的、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夺追寻一些论文有因此多了,大牛的抑活跃的研究者主页往往提供了他们的新式研究线索,顺便还可八瞬间各位大牛的更,对于我这样的多少菜鸟来说最好极致管用的凡奇迹可以找到源码,很多早晚只是看论文是料理不到头思路的。

1 牛人Homepages(随意排序,不分开次):

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,欧洲无限好的几乎单CV/ML研究机构;

3.Helmut Grabner:Online
Boosting and Vision的作者,tracking by online feature
selection的头经典,貌似现在休是非常活跃了,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,华人学者中之高明;

6.Junsong
Yuan:NTU,上面Wu老师的学员;

7.James W.
Davis:俄亥俄州立,视频监控;

8. The Australian Centre for Visual
Technologies:阿德莱德大学之CV组,最近吗是exceedingly
active & fruitful;

9.Chunhua
Shen:属者的ACVT组,最近特别活跃;

10.Xi
Li:同属ACVT,之前是中科院的PHD,跟踪点的舆论多,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,在线上,跟踪/检测等,active!源码分享;

13.Statistical Visual Computing
Laboratory:UCSD,光听名字便异常学术吧,Saliency研究很有叫;

14.David
Ross:多伦多大学,IVT的撰稿人,跟踪中Generative表观的经被的藏,提供源码,IVT的代码结构被新兴广大总人口引用,值得一读;

15.EPFL, Computer Vision
Laboratory:洛桑理工的院,和点的底ETHZ CV
lab同样是欧洲极好之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,目前势头作为分析及多目标跟踪等;

19.Shai
Avidan:特拉维夫大学,大牛级,可终Tracking-by-detection的缔造者,Ensemble
Tracking, SVM Tracking;

20.Visual Information Processing and
Learning:中科院计算所,山世光先生的研究组,不待介绍了吧;

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,稀疏代表;

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长条场;

28.Computational Vision
Group:Leader–Deva
Ramanan;

29.Zhang
Lei:香港理工,稀疏代表,人脸识别,可以算大中华区比较活泼的研究组了,几乎各个首论文都起指向诺源码

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,貌似是个不利的topic;

32.Loris Bazzani:person
re-id,他的SDALF(code)描述子经常让用来开呢比较对象,说明要发生参考价值的;

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(主页有无数论文代码)

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.

 

 

 

 

最主要研究组:

 

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 私有、研究机关链接

(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国语网站;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)微软亚洲研究院处理器视觉研究组; 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、跟踪的测试视频下充斥;http://apps.hi.baidu.com/share/detail/18903287
(24)香港中文大学助理教学王晓刚; 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暨利用(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及模式识别研究所:http://www.pami.sjtu.edu.cn/

(37)上海交通大学处理器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)中科院生物识别及安全技术研究中心:http://www.cbsr.ia.ac.cn/china/index%20CH.asp

(43)瑞士巴塞尔大学 Thomas
Vetter教学: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)美国伊利诺伊州即大学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、运动和认识实验室: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)普林斯顿大学讲授李凯:http://www.cs.princeton.edu/~li/
(70)普林斯顿大学博士贾登:http://www.cs.princeton.edu/~jiadeng/
(71)牛津大学教书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)英国爱丁堡大学讲授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书David Lowe: http://www.cs.ubc.ca/~lowe/
(79)英国爱丁堡大学教学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)多伦多大学讲授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)康奈尔大学讲授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)哥伦比亚大学教书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)康奈尔大学讲授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员教授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)香港中文大学讲授贾佳亚: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)布朗大学讲授Benjamin Kimia: http://www.lems.brown.edu/kimia.html 

(109)数据堂-图像处理相关的样书数量:http://www.datatang.com/data/list/602026/p1

(110)东软依据CV的汽车拉驾驶系统: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副教授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)法国读书算法和网实验室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)以色列技术学院教书Michael
Elad:http://www.cs.technion.ac.il/~elad/

(123)韩国长庚大学计算机视觉及模式识别实验室: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)康奈尔大学视觉以及图像分析组:http://www.via.cornell.edu/
医学图像处理

(130)密西根州即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和遥感实验室:http://www.cv.tu-berlin.de/menue/computer\_vision\_remote\_sensing/parameter/en/
图像分析、物体重建、基于图像的标测量、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/
运动检测和跟、视频压缩、3D重建、字符定位

(133)英国萨利大学视觉、语音以及信号处理为主:
http://www.surrey.ac.uk/cvssp/   人脸识别、监控、3D、视频查找、
(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、运动检测及析、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)百度技术可总监给凯:http://www.dbs.ifi.lmu.de/~yu\_k/
深度上,稀疏代表,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、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)美国坦桑尼亚州顿时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/
 人的态势估计跟跟踪

(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/ 跟踪以及机器人导航

(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/ 目标检测及辨识;

(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,目标检测和跟踪;

(155)魏茨曼是研究所教授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/ 深度上

(161)凤巢系统架构师张栋博士:http://weibo.com/machinelearning

(162)2012年上星计划机器上课程: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、质量评议

(164)图像视频质量评比: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跟多媒体研究为主http://www.cs.uu.nl/centers/give/multimedia/index.html 图像、多媒体检索和配合

(170)英国格拉斯哥大学信搜索小组:http://ir.dcs.gla.ac.uk/ 文本、图像、视频查找

(171)中科院自动化所孙哲南副教授: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)清华大学副教授冯建江: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)中山大学副教授郑伟诗: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)百度深度上研讨为主博士后余轶南: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、去叫

(182)美国加州大学Peyman
Milanfar教授:http://users.soe.ucsc.edu/~milanfar/ 去噪

(183)中科院计算所符合研究员常虹:http://www.jdl.ac.cn/user/hchang/index.html 图像检索、半监督上、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)密西根大学副教授Honglak
Lee: http://web.eecs.umich.edu/~honglak/ 机器学习、特征提取,去叫、稀疏代表;

(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/ 稀疏代表,超分辨率、图片检索、去叫、去模糊

(190)Deep
Learning主页:http://deeplearning.net/ 深度上论文、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,深度上

(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大学副教授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 深度上、去叫、编码、压缩感知、超分辨率、聚类、分割等有关代码集合

(196)西班牙格拉纳达大学超过分辨率重建项目组: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
 内产生广大言语识别、字符识别的教练,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、汽车拉驾驶

(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

 

平等、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]

第二、图像分割Image Segmentation:

  • Normalized Cut [1] [Matlab
    code]

  • Gerg Mori’ Superpixel code [2] [Matlab
    code]

  • Efficient Graph-based Image Segmentation [3] [C++
    code] [Matlab
    wrapper]

  • Mean-Shift Image Segmentation [4] [EDISON C++
    code]
    [Matlab
    wrapper]

  • OWT-UCM Hierarchical Segmentation [5]
    [Resources]

  • Turbepixels [6] [Matlab code
    32bit]
    [Matlab code
    64bit]
    [Updated
    code]

  • Quick-Shift [7]
    [VLFeat]

  • SLIC Superpixels [8]
    [Project]

  • Segmentation by Minimum Code Length [9]
    [Project]

  • Biased Normalized Cut [10]
    [Project]

  • Segmentation Tree [11-12]
    [Project]

  • Entropy Rate Superpixel Segmentation [13]
    [Code]

  • Fast Approximate Energy Minimization via Graph
    Cuts[Paper][Code]

  • Efficient Planar Graph Cuts with Applications in Computer
    Vision[Paper][Code]

  • Isoperimetric Graph Partitioning for Image
    Segmentation[Paper][Code]

  • Random Walks for Image
    Segmentation[Paper][Code]

  • Blossom V: A new implementation of a minimum cost perfect matching
    algorithm[Code]

  • An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy
    Minimization in Computer
    Vision[Paper][Code]

  • Geodesic Star Convexity for Interactive Image
    Segmentation[Project]

  • Contour Detection and Image Segmentation
    Resources[Project][Code]

  • Biased Normalized
    Cuts[Project]

  • Max-flow/min-cut[Project]

  • Chan-Vese Segmentation using Level
    Set[Project]

  • A Toolbox of Level Set
    Methods[Project]

  • Re-initialization Free Level Set Evolution via Reaction
    Diffusion[Project]

  • Improved C-V active contour
    model[Paper][Code]

  • A Variational Multiphase Level Set Approach to Simultaneous
    Segmentation and Bias
    Correction[Paper][Code]

  • Level Set Method Research by Chunming
    Li[Project]

  • ClassCut for Unsupervised Class
    Segmentation[code]

  • SEEDS: Superpixels Extracted via Energy-Driven
    Sampling [[Project](http://www.vision.ee.ethz.ch/~vamichae/seeds/)\]\[[other](http://www.mvdblive.org/seeds/)\]

其三、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]

季、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、机器上算法:

  • 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、人手和手指检测及识别:

  • 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](http://www.ee.columbia.edu/~wliu/CVPR08_ssml.pdf)\]\[[code](http://www.ee.columbia.edu/~wliu/SSMetric.zip)\]

十八、马尔科夫以机场Markov Random Fields:

  • Markov Random Fields for
    Super-Resolution [[](http://www.ee.columbia.edu/~wliu/CVPR08_ssml.pdf)[Project](http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.html)\]

  • 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 [[](http://www.ee.columbia.edu/~wliu/CVPR08_ssml.pdf)[Project](http://www.visionbib.com/bibliography/motion-i763.html)\]

  • 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](http://www.edwardrosten.com/work/fast.html) – High-speed
    corner detector implementation for a wide variety of platforms

-   [AGAST](http://www6.in.tum.de/Main/ResearchAgast) – 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](http://cvlab.epfl.ch/software/brief/) – C++ code for a
    fast and accurate interest point descriptor (not invariant to
    rotations and scale) (ECCV 2010)

-   [ORB](http://docs.opencv.org/modules/features2d/doc/feature_detection_and_description.html) –
    OpenCV implementation of the Oriented-Brief (ORB) descriptor
    (invariant to rotations, but not scale)

-   [BRISK](http://www.asl.ethz.ch/people/lestefan/personal/BRISK) –
    Efficient Binary descriptor invariant to rotations and scale. It
    includes a Matlab mex interface. (ICCV 2011)

-   [FREAK](http://www.ivpe.com/freak.htm) – Faster than BRISK
    (invariant to rotations and scale) (CVPR 2012)



SIFT and SURF Implementations:

-   SIFT: [VLFeat](http://www.vlfeat.org/), [OpenCV](http://docs.opencv.org/modules/nonfree/doc/feature_detection.html), [Original
    code](http://www.cs.ubc.ca/~lowe/keypoints/) by David Lowe, [GPU
    implementation](http://cs.unc.edu/~ccwu/siftgpu/), [OpenSIFT](http://robwhess.github.com/opensift/)

-   SURF: [Herbert Bay’s
    code](http://www.vision.ee.ethz.ch/~surf/), [OpenCV](http://docs.opencv.org/modules/nonfree/doc/feature_detection.html), [GPU-SURF](http://www.visual-experiments.com/demos/gpusurf/)



Other Local Feature Detectors and Descriptors:

-   [VGG Affine Covariant
    features](http://www.robots.ox.ac.uk/~vgg/research/affine/) –
    Oxford code for various affine covariant feature detectors and
    descriptors.

-   [LIOP
    descriptor](http://vision.ia.ac.cn/Students/wzh/publication/liop/index.html) –
    Source code for the Local Intensity order Pattern (LIOP)
    descriptor (ICCV 2011).

-   [Local Symmetry
    Features](http://www.cs.cornell.edu/projects/symfeat/) – Source
    code for matching of local symmetry features under large
    variations in lighting, age, and rendering style (CVPR 2012).



Global Image Descriptors:

-   [GIST](http://people.csail.mit.edu/torralba/code/spatialenvelope/) –
    Matlab code for the GIST descriptor

-   [CENTRIST](https://sites.google.com/site/wujx2001/home) – Global
    visual descriptor for scene categorization and object detection
    (PAMI 2011)

 

Feature Coding and Pooling

-   [VGG Feature Encoding
    Toolkit](http://www.robots.ox.ac.uk/~vgg/software/enceval_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](http://www.cs.illinois.edu/homes/slazebni/) – Source
    code for feature pooling based on spatial pyramid matching
    (widely used for image classification)

 

Convolutional Nets and Deep Learning

-   [EBLearn](http://eblearn.sourceforge.net/) – C++ Library for
    Energy-Based Learning. It includes several demos and
    step-by-step instructions to train classifiers based on
    convolutional neural networks.

-   [Torch7](http://www.torch.ch/) – Provides a matlab-like
    environment for state-of-the-art machine learning algorithms,
    including a fast implementation of convolutional neural
    networks.

-   [Deep Learning](http://deeplearning.net/software_links/) -
    Various links for deep learning software.

 

Part-Based Models

 

-   [Deformable Part-based
    Detector](http://people.cs.uchicago.edu/~rbg/latent/) – Library
    provided by the authors of the original paper (state-of-the-art
    in PASCAL VOC detection task)

-   [Efficient Deformable Part-Based
    Detector](http://vision.mas.ecp.fr/Personnel/iasonas/dpms.html) –
    Branch-and-Bound implementation for a deformable part-based
    detector.

-   [Accelerated Deformable Part
    Model](http://www.idiap.ch/~cdubout/coding.html) – 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](http://iselab.cvc.uab.es/CoarseToFine) – Fast approach
    for deformable object detection (CVPR 2011).

-   [Poselets](http://www.eecs.berkeley.edu/~lbourdev/poselets/) –
    C++ and Matlab versions for object detection based on poselets.

-   [Part-based Face Detector and Pose
    Estimation](http://www.ics.uci.edu/~xzhu/face/) – Implementation
    of a unified approach for face detection, pose estimation, and
    landmark localization (CVPR 2012).

     

    Attributes and Semantic Features

    -   [Relative
        Attributes](http://ttic.uchicago.edu/~dparikh/relative.html#code) –
        Modified implementation of RankSVM to train Relative
        Attributes (ICCV 2011).

    -   [Object
        Bank](http://vision.stanford.edu/projects/objectbank/) –
        Implementation of object bank semantic features (NIPS 2010).
        See
        also [ActionBank](http://www.cse.buffalo.edu/~jcorso/r/actionbank/)

    -   [Classemes, Picodes, and Meta-class
        features](http://vlg.cs.dartmouth.edu/projects/vlg_extractor/vlg_extractor/Home.html) –
        Software for extracting high-level image descriptors (ECCV
        2010, NIPS 2011, CVPR 2012).

    Large-Scale Learning

    -   [Additive
        Kernels](http://ttic.uchicago.edu/~smaji/projects/fiksvm/) –
        Source code for fast additive kernel SVM classifiers (PAMI
        2013).

    -   [LIBLINEAR](http://www.csie.ntu.edu.tw/~cjlin/liblinear/) –
        Library for large-scale linear SVM classification.

    -   [VLFeat](http://www.vlfeat.org/) – Implementation for
        Pegasos SVM and Homogeneous Kernel map.

    Fast Indexing and Image Retrieval

    -   [FLANN](http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN) –
        Library for performing fast approximate nearest neighbor.

    -   [Kernelized
        LSH](http://www.cse.ohio-state.edu/~kulis/klsh/klsh.htm) –
        Source code for Kernelized Locality-Sensitive Hashing (ICCV
        2009).

    -   [ITQ Binary codes](http://www.unc.edu/~yunchao/itq.htm) –
        Code for generation of small binary codes using Iterative
        Quantization and other baselines such as
        Locality-Sensitive-Hashing (CVPR 2011).

    -   [INRIA Image
        Retrieval](http://lear.inrialpes.fr/src/inria_fisher/) –
        Efficient code for state-of-the-art large-scale image
        retrieval (CVPR 2011).

    Object Detection

    -   See [Part-based
        Models](http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html#parts) and [Convolutional
        Nets](http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html#convnets) above.

    -   [Pedestrian Detection at
        100fps](https://bitbucket.org/rodrigob/doppia) – Very fast
        and accurate pedestrian detector (CVPR 2012).

    -   [Caltech Pedestrian Detection
        Benchmark](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/) –
        Excellent resource for pedestrian detection, with various
        links for state-of-the-art implementations.

    -   [OpenCV](http://docs.opencv.org/trunk/modules/objdetect/doc/cascade_classification.html?highlight=face%20detection) –
        Enhanced implementation of Viola&Jones real-time object
        detector, with trained models for face detection.

    -   [Efficient Subwindow
        Search](https://sites.google.com/site/christophlampert/software) –
        Source code for branch-and-bound optimization for efficient
        object localization (CVPR 2008).

    3D Recognition

    -   [Point-Cloud Library](http://www.pointclouds.org/) – Library
        for 3D image and point cloud processing.

    Action Recognition

    -   [ActionBank](http://www.cse.buffalo.edu/~jcorso/r/actionbank/) –
        Source code for action recognition based on the ActionBank
        representation (CVPR 2012).

    -   [STIP
        Features](http://www.di.ens.fr/~laptev/download.html) –
        software for computing space-time interest point descriptors

    -   [Independent Subspace
        Analysis](http://ai.stanford.edu/~quocle/) – Look for
        Stacked ISA for Videos (CVPR 2011)

    -   [Velocity Histories of Tracked
        Keypoints](http://www.cs.rochester.edu/~rmessing/uradl/) -
        C++ code for activity recognition using the velocity
        histories of tracked keypoints (ICCV 2009)

    ------------------------------------------------------------------------

    Datasets

    Attributes

    -   [Animals with
        Attributes](http://attributes.kyb.tuebingen.mpg.de/) –
        30,475 images of 50 animals classes with 6 pre-extracted
        feature representations for each image.

    -   [aYahoo and
        aPascal](http://vision.cs.uiuc.edu/attributes/) – Attribute
        annotations for images collected from Yahoo and Pascal
        VOC 2008.

    -   [FaceTracer](http://www.cs.columbia.edu/CAVE/databases/facetracer/) –
        15,000 faces annotated with 10 attributes and fiducial
        points.

    -   [PubFig](http://www.cs.columbia.edu/CAVE/databases/pubfig/) –
        58,797 face images of 200 people with 73 attribute
        classifier outputs.

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

    -   [Human
        Attributes](http://www.eecs.berkeley.edu/~lbourdev/poselets/) –
        8,000 people with annotated attributes. Check also
        this [link](https://sharma.users.greyc.fr/hatdb/) for
        another dataset of human attributes.

    -   [SUN Attribute
        Database](http://cs.brown.edu/~gen/sunattributes.html) –
        Large-scale scene attribute database with a taxonomy of 102
        attributes.

    -   [ImageNet
        Attributes](http://www.image-net.org/download-attributes) –
        Variety of attribute labels for the ImageNet dataset.

    -   [Relative
        attributes](http://ttic.uchicago.edu/~dparikh/relative.html#data) –
        Data for OSR and a subset of PubFig datasets. Check also
        this [link](http://vision.cs.utexas.edu/whittlesearch/) for
        the WhittleSearch data.

    -   [Attribute Discovery
        Dataset](http://tamaraberg.com/attributesDataset/index.html) –
        Images of shopping categories associated with textual
        descriptions.

    Fine-grained Visual Categorization

    -   [Caltech-UCSD Birds
        Dataset](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html) –
        Hundreds of bird categories with annotated parts and
        attributes.

    -   [Stanford Dogs
        Dataset](http://vision.stanford.edu/aditya86/ImageNetDogs/) –
        20,000 images of 120 breeds of dogs from around the world.

    -   [Oxford-IIIT Pet
        Dataset](http://www.robots.ox.ac.uk/~vgg/data/pets/) – 37
        category pet dataset with roughly 200 images for each class.
        Pixel level trimap segmentation is included.

    -   [Leeds Butterfly
        Dataset](http://www.comp.leeds.ac.uk/scs6jwks/dataset/leedsbutterfly/) –
        832 images of 10 species of butterflies.

    -   [Oxford Flower
        Dataset](http://www.robots.ox.ac.uk/~vgg/data/flowers/) –
        Hundreds of flower categories.

    Face Detection

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

    -   [CMU/MIT](http://vasc.ri.cmu.edu/idb/html/face/frontal_images/index.html) –
        Classical face detection dataset.

    Face Recognition

    -   [Face Recognition
        Homepage](http://www.face-rec.org/databases/) – Large
        collection of face recognition datasets.

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

    -   [NIST Face
        Homepage](http://www.nist.gov/itl/iad/ig/face.cfm) –
        includes face recognition grand challenge (FRGC), vendor
        tests (FRVT) and others.

    -   [CMU Multi-PIE](http://www.multipie.org/) – contains more
        than 750,000 images of 337 people, with 15 different views
        and 19 lighting conditions.

    -   [FERET](http://www.nist.gov/itl/iad/ig/colorferet.cfm) –
        Classical face recognition dataset.

    -   [Deng Cai’s face dataset in Matlab
        Format](http://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html) –
        Easy to use if you want play with simple face datasets
        including Yale, ORL, PIE, and Extended Yale B.

    -   [SCFace](http://www.scface.org/) – Low-resolution face
        dataset captured from surveillance cameras.

    Handwritten Digits

    -   [MNIST](http://yann.lecun.com/exdb/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](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/) –
        10 hours of video taken from a vehicle,350K bounding boxes
        for about 2.3K unique pedestrians.

    -   [INRIA Person
        Dataset](http://pascal.inrialpes.fr/data/human/) – Currently
        one of the most popular pedestrian detection datasets.

    -   [ETH Pedestrian
        Dataset](http://www.vision.ee.ethz.ch/~aess/dataset/) –
        Urban dataset captured from a stereo rig mounted on a
        stroller.

    -   [TUD-Brussels Pedestrian
        Dataset](http://www.d2.mpi-inf.mpg.de/tud-brussels) –
        Dataset with image pairs recorded in an crowded urban
        setting with an onboard camera.

    -   [PASCAL Human
        Detection](http://pascallin.ecs.soton.ac.uk/challenges/VOC/) –
        One of 20 categories in PASCAL VOC detection challenges.

    -   [USC Pedestrian
        Dataset](http://iris.usc.edu/Vision-Users/OldUsers/bowu/DatasetWebpage/dataset.html) –
        Small dataset captured from surveillance cameras.

    Generic Object Recognition

    -   [ImageNet](http://www.image-net.org/) – Currently the
        largest visual recognition dataset in terms of number of
        categories and images.

    -   [Tiny
        Images](http://groups.csail.mit.edu/vision/TinyImages/) – 80
        million 32x32 low resolution images.

    -   [Pascal
        VOC](http://pascallin.ecs.soton.ac.uk/challenges/VOC/) – One
        of the most influential visual recognition datasets.

    -   [Caltech
        101](http://www.vision.caltech.edu/Image_Datasets/Caltech101/) / [Caltech
        256](http://www.vision.caltech.edu/Image_Datasets/Caltech256/) –
        Popular image datasets containing 101 and 256 object
        categories, respectively.

    -   [MIT
        LabelMe](http://new-labelme.csail.mit.edu/Release3.0/index.php) –
        Online annotation tool for building computer vision
        databases.

    Scene Recognition

    -   [MIT SUN Dataset](http://groups.csail.mit.edu/vision/SUN/) –
        MIT scene understanding dataset.

    -   [UIUC Fifteen Scene
        Categories](http://www-cvr.ai.uiuc.edu/ponce_grp/data/) –
        Dataset of 15 natural scene categories.

    Feature Detection and Description

    -   [VGG Affine
        Dataset](http://www.robots.ox.ac.uk/~vgg/data/data-aff.html) –
        Widely used dataset for measuring performance of feature
        detection and description.
        Check[VLBenchmarks](http://www.vlfeat.org/benchmarks/index.html)for
        an evaluation framework.

    Action Recognition

    -   [Benchmarking Activity
        Recognition](http://rogerioferis.com/VisualRecognitionAndSearch/material/LiuFerisSunTutorial.pdf) –
        CVPR 2012 tutorial covering various datasets for action
        recognition.

    RGBD Recognition

    -   [RGB-D Object
        Dataset](http://www.cs.washington.edu/rgbd-dataset/index.html) –
        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](http://www.vision.ee.ethz.ch/software/index.de.html(%E5%BD%93%E7%84%B6%E8%BF%99%E5%8F%AA%E6%98%AF%E5%85%B6%E4%B8%AD%E4%B9%8B%E4%B8%80)(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](http://mi.eng.cam.ac.uk/~er258/work/fast.html)

    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](http://192.168.1.27/wiki/MultiBoost)(不说啥了,多类Adaboost算法的程序):<http://sourceforge.net/projects/multiboost/>

    -   [TextonBoost](http://192.168.1.27/wiki/TextonBoost)(我们教研室王冠夫师兄的毕设): [Jamie
        Shotton - Code](http://jamie.shotton.org/work/code.html)

    -   [LibSvm](http://192.168.1.27/wiki/LibSvm)的老爹(推荐): <http://www.csie.ntu.edu.tw/~cjlin/>

    -   [Conditional Random
        Fields](http://www.inference.phy.cam.ac.uk/hmw26/crf/)(CRF论文+Code列表,推荐)

    -   [CRF++: Yet Another CRF
        toolkit](http://crfpp.sourceforge.net/)

    -   [Conditional Random Field (CRF) Toolbox for
        Matlab](http://www.computervisiononline.com/software/conditional-random-field-crf-toolbox-matlab)

    -   [Tree CRFs](http://www.cs.cmu.edu/~jkbradle/TreeCRFs/)

    -   [LingPipe:
        Installation](http://alias-i.com/lingpipe/web/install.html)

    -   [Hidden Markov
        Models](http://jedlik.phy.bme.hu/~gerjanos/HMM/node2.html)(推荐)

    -   [隐马尔科夫模型](http://blog.csdn.net/eaglex/article/details/6376826)[(Hidden
        Markov
        Models)](http://blog.csdn.net/eaglex/article/details/6376826)[系列之一](http://blog.csdn.net/eaglex/article/details/6376826)[ -
        eaglex](http://blog.csdn.net/eaglex/article/details/6376826)[的专栏 -
        博客频道 ](http://blog.csdn.net/eaglex/article/details/6376826)[-
        CSDN.NET](http://blog.csdn.net/eaglex/article/details/6376826)[(推荐)](http://blog.csdn.net/eaglex/article/details/6376826)

    综合代码

    -   [CvPapers](http://192.168.1.27/wiki/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 Naïve 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](http://pablonegri.free.fr/index.html)

    -   [Jianxin Wu's
        homepage](http://c2inet.sce.ntu.edu.sg/Jianxin/index.html)

    -   [Peter Carbonetto](http://www.cs.ubc.ca/~pcarbo/)

    -   [Markov Random Fields for
        Super-Resolution](http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.html)

    -   [Detecting and Sketching the
        Common](http://www.wisdom.weizmann.ac.il/~vision/SketchTheCommon/)

    -   [Pedro Felzenszwalb](http://people.cs.uchicago.edu/~pff/)

    -   [Hae JONG,
        SEO](http://users.soe.ucsc.edu/~rokaf/interests.html)

    -   [CAP 5416 - Computer
        Vision](http://www.cise.ufl.edu/class/cap5416fa09/Projects.html)

    -   [Parallel Tracking and Mapping for Small AR Workspaces
        (PTAM)](http://www.robots.ox.ac.uk/~gk/PTAM/)

    -   [Deva Ramanan - UC Irvine - Computer
        Vision](http://www.ics.uci.edu/~dramanan/)

    -   [Raghuraman Gopalan](http://www.umiacs.umd.edu/~raghuram/)

    -   [Hui Kong](http://bmi.osu.edu/~hkong/index.htm)

    -   [Jamie Shotton - Post-Doctoral Researcher in Computer
        Vision](http://jamie.shotton.org/work/index.html)

    -   [Jean-Yves
        AUDIBERT](http://imagine.enpc.fr/~audibert/index.html)

    -   [Olga Veksler](http://www.csd.uwo.ca/~olga/)

    -   [Stephen
        Gould](http://users.cecs.anu.edu.au/~sgould/index.html#software)

    -   [Publications (Last Update:
        09/30/10)](http://faculty.ucmerced.edu/mhyang/code.html)

    -   [Karim Ali -
        FlowBoost](http://cvlab.epfl.ch/~ali/flowboost.htm)

    -   [A simple parts and structure object
        detector](http://people.csail.mit.edu/fergus/iccv2005/partsstructure.html)

    -   [Code - Oxford Brookes Vision
        Group](http://cms.brookes.ac.uk/research/visiongroup/code.php)

    -   [Taku Kudo](http://chasen.org/~taku/index.html.en)

    行人检测

    -   [Histogram of Oriented Gradient
        (Windows)](http://www.computing.edu.au/~12482661/hog.html)

    -   [INRIA Pedestrian
        detector](http://www.cs.berkeley.edu/~smaji/projects/ped-detector/)

    -   [Poselets](http://www.eecs.berkeley.edu/~lbourdev/poselets/)

    -   [William Robson Schwartz -
        Softwares](http://www.liv.ic.unicamp.br/~wschwartz/softwares.html)

    -   [calvin upper-body detector
        v1.02](http://www.vision.ee.ethz.ch/~calvin/calvin_upperbody_detector/)

    -   [RPT@CVG](http://www.cvg.rdg.ac.uk/software/rpt/index.html)

    -   [Main
        Page](http://www.idiap.ch/~odobez/human-detection/index.html)

    -   [Source
        Code](http://www.lienhart.de/Source_Code/source_code.html)

    -   [Dr. Luciano
        Spinello](http://www.informatik.uni-freiburg.de/~spinello/people2D.html)

    -   [Pedestrian
        Detection](http://bmi.osu.edu/~hkong/Human_Detection.html)

    -   [Class-Specific Hough Forests for Object
        Detection](http://www.vision.ee.ethz.ch/~gallju/projects/houghforest/index.html)

    -   [Jianxin Wu's
        homepage](http://c2inet.sce.ntu.edu.sg/Jianxin/index.html)(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](http://www.cs.cornell.edu/~asaxena/rccar/)

    -   [TLD](http://info.ee.surrey.ac.uk/Personal/Z.Kalal/tld.html)(2010年很火的tracking算法)

    -   [online boosting
        trackers](http://www.vision.ee.ethz.ch/boostingTrackers/)

    -   [Boris
        Babenko](http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml)

    -   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/](http://of-.sourceforge.net/)

    物体检测算法

    -   [Object
        Detection](http://www.irisa.fr/vista/Equipe/People/Laptev/objectdetection.html)

    -   [Software for object
        detection](http://www.seas.upenn.edu/~limingw/obj_det_accv07/code.html)

    人脸检测

    -   [Source
        Code](http://www.lienhart.de/Source_Code/source_code.html)

    -   [10个人脸检测项目](http://itp.nyu.edu/~mbe230/blogmer/2011/02/10-face-detection-projects/)

    -   [Jianxin Wu's
        homepage](http://c2inet.sce.ntu.edu.sg/Jianxin/index.html)(又是这货)

    ICA独立成分分析

    -   [An ICA page-papers,code,demo,links (Tony
        Bell)](http://cnl.salk.edu/~tony/ica.html)

    -   [FastICA](http://research.ics.tkk.fi/ica/fastica/)

    -   [Cached k-d tree search for ICP
        algorithms](http://kos.informatik.uni-osnabrueck.de/download/3dim2007/paper.html)

    滤波算法

    -   卡尔曼滤波:[The Kalman
        Filter](http://www.cs.unc.edu/~welch/kalman/index.html)(终极网页)

    -   Bayesian Filtering Library: [The Bayesian Filtering
        Library](http://www.orocos.org/bfl)

    路面识别

    -   [Source
        Code](http://www.multimedia-computing.de/wiki/Source_Code#Dataset_of_logos_in_real-world_images:_FlickrLogos-32)

    -   [Vanishing point detection for general road
        detection](http://bmi.osu.edu/~hkong/Road_Detection.html)

    分割算法

    -   MATLAB Normalized Cuts Segmentation
        Code:[software](http://www.cis.upenn.edu/~jshi/software/)

    -   超像素分割:[SLIC
        Superpixels](http://ivrg.epfl.ch/supplementary_material/RK_SLICSuperpixels/index.html)

    -   

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

汇集不周到,欢迎补全!!更多,请关注http://blog.csdn.net/tiandijun/

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