微机视觉牛人博客和代码汇总

各样做过或者正在做琢磨工作的人都会关注一些和好认为有价值的、活跃的啄磨组和民用的主页,关注他们的主页有时候比盲目的去搜寻一些舆论有用多了,大牛的或者活跃的探究者主页往往提供了她们的风靡探讨线索,顺便还可八一晃各位大牛的阅历,对于我这样的小菜鸟来说最最实惠的是奇迹可以找到源码,很多时候光看随笔是理不清思路的。

1 牛人Homepages(随意排序,不分先后):

1.USC Computer Vision
Group
:南加大,多指标跟踪/检测等;

2.ETHZ Computer Vision
Laboratory
:华盛顿(Washington)联邦师范高校,非洲最好的多少个CV/ML研讨机构;

3.Helmut Grabner:Online
Boosting and Vision的撰稿人,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
:美利哥西交大学,华人学者中的翘楚;

6.Junsong
Yuan
:NTU,上边Wu老师的学生;

7.James W.
Davis
:爱荷华州立,视频监控;

8. The Australian Centre for Visual
Technologies
:阿德莱德(Adelaide)大学的CV组,目前也是exceedingly
active & fruitful;

9.Chunhua
Shen
:属地方的ACVT组,目前特别活跃;

10.Xi
Li
:同属ACVT,在此之前是中科院的PHD,跟踪方面的随想很多,有理论深度;

11.Haibin
Ling
:天普学院,L1-Tracker及后续扩张,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探讨很有名;

14.David
Ross
:约翰内斯堡学院,IVT的作者,跟踪中Generative表观的经典中的经典,提供源码,IVT的代码结构被新兴广大人引用,值得一读;

15.EPFL, Computer Vision
Laboratory
:都林理工的高校,和地点的的ETHZ CV
lab同样是南美洲最好的CV探究大组;

16.Jamie
Shotton
:属微软澳大利亚国立钻探为主,Decision/Regression
Forests

17.Sinisa
Todorovic
:内华达州立,行为分析等;

18.Shi Jianbo:大名鼎鼎的Good Feature
to Track作者,目前大势作为分析和多目的跟踪等;

19.Shai
Avidan
:马尼拉高校,大牛级,可算是Tracking-by-detection的成立者,Ensemble
Tracking, SVM Tracking;

20.Visual Information Processing and
Learning
:中科院总计所,山世光先生的探究组,不需介绍了啊;

21.Shaogang Gong:Queen Mary
University of London,各种PAMI,IJCV;

22.Yang
Jian
:福冈电子科技高校,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
:下面Wang老师学士硕士,群体行为,看看人家的Publications已经轻松甩国内大学生好几条街;

28.Computational Vision
Group
:Leader–Deva
Ramanan

29.Zhang
Lei
:加州理工,稀疏代表,人脸识别,可以算大中华区相比活跃的探讨组了,几乎每篇杂谈都有对应源码

30.Zhang
Kaihua
:上边Zhang老师学生,Compressive
Tracking

31.Pramod
Sharma
:离线磨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)描述子通常被用来做为相比对象,表明或者有参考价值的;

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(主页有这个舆论代码)

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.

 

 

 

 

首要琢磨组:

 

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 个体、探讨单位链接

(1)googleResearch; http://research.google.com/index.html
(2)MIT研究生,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高校硕士崔靖宇; http://www.stanford.edu/~jycui/
(7)UCLA助教朱松纯; 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商员; http://www.cbsr.ia.ac.cn/users/szli/
(12)中科院总括所山世光探究员; http://www.jdl.ac.cn/user/sgshan/
(13)人脸识别主页; http://www.face-rec.org/
(14)加州大学Berkeley分校CV小组;http://www.eecs.berkeley.edu/Research/Projects/CS/vision/

(15)南加州高校CV实验室; http://iris.usc.edu/USC-Computer-Vision.html
(16)卡内基(Carnegie)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)微软北美洲探究院处理器视觉研商组; http://research.microsoft.com/en-us/groups/vc/
(19)微软宾夕法尼亚州立啄磨院ML与CV研商组; http://research.microsoft.com/en-us/groups/mlp/default.aspx

(20)研学论坛; http://bbs.matwav.com/
(21)U.S.Rutgers大学助理助教刘青山;http://www.research.rutgers.edu/~qsliu/
w88win优德手机版,(22)总结机视觉最新资讯网; 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(Hong Kong)中文高校助理员教师王晓刚; http://www.ee.cuhk.edu.hk/~xgwang/
(25)Hong Kong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)河复旦学图像技术探讨与利用(ITRA)团队:http://www.dvzju.com/

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

(34)交大大学章毓晋讲师:http://www.tsinghua.edu.cn/publish/ee/4157/2010/20101217173552339241557/20101217173552339241557_.html

(35)一流民用机器人商量小组Porf.加里(Gary)领导的威尔ow
Garage:http://www.willowgarage.com/

(36)新加坡政法大学图像处理与情势识别研讨所:http://www.pami.sjtu.edu.cn/

(37)香港中医药大学处理器视觉实验室刘允才助教:http://www.visionlab.sjtu.edu.cn/

(38)怀俄明州高校奥斯汀(Austen)分校助理讲师Kristen Grauman
http://www.cs.utexas.edu/~grauman/ 图像分解,检索

(39)厦大高校电子工程系智能图文信息处理实验室(丁晓青讲师):http://ocrserv.ee.tsinghua.edu.cn/auto/index.asp

(40)上海大学高文助教:http://www.jdl.ac.cn/htm-gaowen/

(41)北大高校艾海舟助教:http://media.cs.tsinghua.edu.cn/cn/aihz

(42)中科院生物识别与白城技能探究为主:http://www.cbsr.ia.ac.cn/china/index%20CH.asp

(43)瑞士海法学院 托马斯(Thomas)(Thomas)Vetter教学:http://informatik.unibas.ch/personen/vetter_t.html

(44)密苏里州立大学 RobHess硕士:http://blogs.oregonstate.edu/hess/

(45)蒙特利尔大学 于仕祺副讲师:http://yushiqi.cn/

(46)马尔默医科大学人工智能与机器人讨论所:http://www.aiar.xjtu.edu.cn/

(47)Carnegie梅隆大学探讨员罗伯特(Robert)(Bert) T.
Collins(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)美国新罕布什尔州立大学生物识别探讨组(Anil K.
Jain助教):http://www.cse.msu.edu/rgroups/biometrics/

(50)美利坚合众国马里兰州立高校托马斯(Thomas) S.
Huang:http://www.beckman.illinois.edu/directory/t-huang1

(51)博洛尼亚高校数字素描测量与统计机视觉商量为主:http://www.whudpcv.cn/index.asp

(52)瑞士联邦哈利法克斯大学山姆(Sam)i
Romdhani助理研讨员:http://informatik.unibas.ch/personen/romdhani_sami/

(53)CMU高校探讨员Yang Wang:http://www.cs.cmu.edu/~wangy/home.html

(54)大英帝国金奈大学提姆(Tim)Cootes教师:http://personalpages.manchester.ac.uk/staff/timothy.f.cootes/

(55)美利哥罗彻斯特大学讲师Jiebo Luo:http://www.cs.rochester.edu/u/jluo/

(56)美利坚合众国普渡高校机器人视觉实验室:https://engineering.purdue.edu/RVL/Welcome.html

(57)美利坚合众国劳斯莱斯州立大学感知、运动与认识实验室:http://vision.cse.psu.edu/home/home.shtml

(58)花旗国加州伯克利分校大学GRASP实验室:https://www.grasp.upenn.edu/

(59)美利坚联邦合众国内达华大学里诺校区CV实验室:http://www.cse.unr.edu/CVL/index.php

(60)美利坚同盟国密西根大学vision实验室:http://www.eecs.umich.edu/vision/index.html

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

(62)华盛顿(华盛顿(Washington))大学研究生后Iva
Kemelmacher:http://www.cs.washington.edu/homes/kemelmi

(63)以色列魏茨曼财经大学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钻探员张正友:http://research.microsoft.com/en-us/um/people/zhang/

(66)中科院自动化所经济学影象探讨室:http://www.3dmed.net/

(67)中科院田捷探讨员:http://www.3dmed.net/tian/

(68)微软Redmond琢磨院研讨员西蒙Baker:http://research.microsoft.com/en-us/people/sbaker/

(69)普林斯顿大学教学李凯:http://www.cs.princeton.edu/~li/
(70)普林斯顿大学研究生贾登:http://www.cs.princeton.edu/~jiadeng/
(71)香港理工高校教书安德鲁(Andrew) Zisserman: http://www.robots.ox.ac.uk/~az/
(72)英国leeds大学钻探员Mark伊夫ringham:http://www.comp.leeds.ac.uk/me/
(73)英帝国伊斯兰堡大学教书克莉丝(Chris)威尔(Will)iam: http://homepages.inf.ed.ac.uk/ckiw/
(74)微软巴黎高等师范探讨院琢磨员约翰(John) Winn: http://johnwinn.org/
(75)北达科他外国语高校教师Monson
H.Hayes:http://savannah.gatech.edu/people/mhayes/index.html
(76)微软北美洲研讨院琢磨员孙剑:http://research.microsoft.com/en-us/people/jiansun/
(77)微软欧洲研讨院商讨员马毅:http://research.microsoft.com/en-us/people/mayi/
(78)英帝国哥伦比亚大学讲师戴维(David) Lowe: http://www.cs.ubc.ca/~lowe/
(79)United Kingdom圣迭戈(Louis)大学教学鲍伯 Fisher: http://homepages.inf.ed.ac.uk/rbf/
(80)加州大学广州分校教师Serge
J.Belongie:http://cseweb.ucsd.edu/~sjb/
(81)马萨诸塞大学教书查理(Charles)(Charles) R.Dyer: http://pages.cs.wisc.edu/~dyer/
(82)莫斯科大学教师Allan.Jepson: http://www.cs.toronto.edu/~jepson/
(83)伦斯勒外国语大学教师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)吉隆坡大学教学:David J.Fleet: http://www.cs.toronto.edu/~fleet/
(86)伦敦(London)大学Mary女王高校教师AndreaCavallaro:http://www.eecs.qmul.ac.uk/~andrea/
(87)阿姆斯特丹高校教书Kyros Kutulakos: http://www.cs.toronto.edu/~kyros/
(88)杜克(杜克(Duke))高校讲授卡尔o 汤姆(Tom)asi: http://www.cs.duke.edu/~tomasi/
(89)CMU教授Martial Hebert: http://www.cs.cmu.edu/~hebert/
(90)MIT助理教师Antonio Torralba: http://web.mit.edu/torralba/www/
(91)爱荷华高校商讨员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大学生田渊栋: 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)哥伦比亚大学教学Sheer K.Nayar: http://www.cs.columbia.edu/~nayar/
(97)三菱电子研讨院探讨员Fatih Porikli :http://www.porikli.com/
(98)康奈尔大学教书Daniel 赫特enlocher:http://www.cs.cornell.edu/~dph/
(99)马那瓜高校讲授周志华:http://cs.nju.edu.cn/zhouzh/index.htm
(100)马德里丰田技术钻探所助理员讲师Devi Parikh:
http://ttic.uchicago.edu/~dparikh/index.html
(101)瑞士农林师范大学学士后Helmut
Grabner:http://www.vision.ee.ethz.ch/~hegrabne/#Short_CV

(102)香江粤语大学讲授贾佳亚:http://www.cse.cuhk.edu.hk/~leojia/index.html

(103)波尔图高校教书吴建鑫:http://c2inet.sce.ntu.edu.sg/Jianxin/index.html

(104)GE探讨院研商员李关:http://www.cs.unc.edu/~lguan/

(105)科罗拉多财经政法大学助教Monson
Hayes:http://savannah.gatech.edu/people/mhayes/

(106)图片检索国际比赛PASCAL
VOC(微软巴黎高等师范探究院协会):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(布朗)高校教书本杰明(Benjamin) Kimia: http://www.lems.brown.edu/kimia.html 

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

(110)东软依据CV的汽车协助驾驶系统:http://www.neusoft.com/cn/solutions/1047/

(111)新罕布什尔大学讲授Rema Chellappa:http://www.cfar.umd.edu/~rama/

(112)孟买丰田琢磨主题助理助教Devi
Parikh:http://ttic.uchicago.edu/~dparikh/index.html

(113)哈佛高校助理教师石建波:http://www.cis.upenn.edu/~jshi/

(114)比尔(Bill)y时鲁汶大学讲师Luc Van
Gool:http://www.vision.ee.ethz.ch/members/get_member.cgi?id=1http://www.vision.ee.ethz.ch/~vangool/

(115)行人检测主页:http://www.pedestrian-detection.com/

(116)法兰西就学算法与系统实验室Basilio
Noris学士:http://lasa.epfl.ch/people/member.php?SCIPER=129576 http://mldemos.epfl.ch/

(117)美利坚合众国缅因大学LARRY S.DAVIS助教:http://www.umiacs.umd.edu/~lsd/

(118)统计机视觉杂文分类导航:http://www.visionbib.com/bibliography/contents.html

(119)总计机视觉分类音讯导航:http://www.visionbib.com/

(120)西班牙伊斯坦布尔金融高校研究生Marcos Nieto:http://marcosnieto.net/

(121)新加坡国立大学副讲师张磊:http://www4.comp.polyu.edu.hk/~cslzhang/

(122)以色列技术高校教书MichaelElad:http://www.cs.technion.ac.il/~elad/

(123)大韩民国启明大学统计机视觉与情势识别实验室:http://cvpr.kmu.ac.kr/

(124)大不列颠及北爱尔兰联合王国诺丁汉大学Michel Valstar学士:http://www.cs.nott.ac.uk/~mfv/

(125)卡内基(Carnegie)梅隆高校Takeo
Kanade讲师:http://www.ri.cmu.edu/people/kanade_takeo.html

(126)微软学术搜索:http://libra.msra.cn/

(127)比尔y时天主教鲁汶大学Radu
提姆(Tim)ofte学士:http://homes.esat.kuleuven.be/~rtimofte/,交通标志检测,定位,3D跟踪

(128)迪斯尼仰光研讨院研商员:Iain
马修s: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/
经济学图像处理

(130)密西根州立硕士物识别探讨组:http://www.cse.msu.edu/biometrics/
人脸识别、指纹识别、图像检索
(131)柏林(Berlin)理工大学电脑视觉与遥感实验室:http://www.cv.tu-berlin.de/menue/computer\_vision\_remote\_sensing/parameter/en/
图像分析、物体重建、基于图像的外表测量、理学图像处理

(132)大不列颠及北爱尔兰联合王国罗利大学数字多媒体商量组: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(Surrey)高校视觉、语音与信号处理为主:
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助教: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)哈佛高校理查德哈特ley讲师:http://users.cecs.anu.edu.au/~hartley/
运动揣摸、稀疏子空间、跟踪、

(136)百度技术副经理于凯:http://www.dbs.ifi.lmu.de/~yu\_k/
深度学习,稀疏表示,图像分类

(137)夏洛特电子农林大学高新波助教:http://web.xidian.edu.cn/xbgao/index.html 质料考评、水印、稀疏表示、超分辨率

(138)加州高校伯克利(Berkeley)(Berkeley)分校Michael(Michael)I.乔丹(Jordan)助教:http://www.cs.berkeley.edu/~jordan/ 机器学习

(139)华盛顿里昂分校行人检测相关资料:http://www.vision.caltech.edu/Image\_Datasets/CaltechPedestrians/

(140)微软Redmond研商院探讨员Piotr
Dollar: http://vision.ucsd.edu/~pdollar/ 行人检测、特征提取、

(141)视觉总计啄磨论坛:http://www.sigvc.org/bbs/
中科院视觉总结商量小组的论坛

(142)美利坚同盟国坦桑尼亚州立高校稀疏学习软件包:http://www.public.asu.edu/~jye02/Software/SLEP/index.htm
稀疏学习

(143)美国加州大学旧金山分校雅各布惠特ehill研究生:http://mplab.ucsd.edu/~jake/ 机器学习

(144)花旗国布朗(布朗(Brown))大学迈克尔(Michael) J.布莱克(Black)讲师:http://cs.brown.edu/~black/
 人的态度揣测和跟踪

(145)美利坚同盟国加州大学圣菲波哥大分校DavidKriegman讲师:http://cseweb.ucsd.edu/~kriegman/ 人脸识别

(146)南加州大学保罗(Paul) Debevec教师: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)北达科他高校D.A.Forsyth讲师:http://luthuli.cs.uiuc.edu/~daf/
三维重建

(148)英国新加坡国立大学IanReid讲师: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(Berkeley)分校Jitendra
Malik讲师:http://www.cs.berkeley.edu/~malik/ 概况检测、图像/录像分割、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/ 目的检测和辨别;

(153)微软研商员保罗(Paul)Viola: http://research.microsoft.com/en-us/um/people/viola/ AdaBoost算法

(154)微软钻探员Antonio
Criminisi: http://research.microsoft.com/en-us/people/antcrim/
图像修补,三维重建,目的检测与跟踪;

(155)魏茨曼科学研商所讲师Michal
Irani: http://www.wisdom.weizmann.ac.il/~irani/ 超分辨率

(156)瑞士联邦卢萨卡农林农林科技高校Pascal
Fua讲师:http://people.epfl.ch/pascal.fua/bio?lang=en 立体视觉,增强现实

(157)康涅狄格科学技术大学Irfan
Essa教师:http://www.ic.gatech.edu/people/irfan-essa 人脸表情识别

(158)中科院助理助教樊彬:http://www.sigvc.org/bfan/ 特征描述;

(159)洛桑联邦理工高校Sebastian
Thrun助教:http://robots.stanford.edu/index.html 机器人;

(160)莫斯科大学杰弗里E.Hinton助教:http://www.cs.toronto.edu/~hinton/ 深度学习

(161)凤巢系统架构师张栋大学生:http://weibo.com/machinelearning

(162)二零一二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讲师:http://www.compsys.ia.ac.cn/people/xiaobaihua.html 文字识别、人脸识别、质地鉴定

(164)图像录像质量评议:http://live.ece.utexas.edu/research/quality/

(165)伦敦大学Yann LeCun助教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讲师:http://cs.brown.edu/~pff/ 特征提取,Deformable Part
Model

(168)南卡罗来纳香槟大学Svetlana
Lazebnik教师:http://www.cs.illinois.edu/homes/slazebni/ 特征提取,聚类,图像检索

(169)荷兰王国乌德勒支高校图像与多媒体研讨为主http://www.cs.uu.nl/centers/give/multimedia/index.html 图像、多媒体检索与配合

(170)大英帝国阿德莱德(Adelaide)大学信息寻找小组: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出手助教:http://www.cbsr.ia.ac.cn/users/znsun/ 虹膜识别、掌纹识别、人脸识别

(172)金沙萨新闻工程大学刘青山讲师:http://www.jstuoke.com/web/xky/detail.asp?NewsID=1096 人脸图像分析、理学图像分析

(173)南开大学助理员讲师冯建江:http://ivg.au.tsinghua.edu.cn/~jfeng/ 指纹识别

(174)北航助理助教黄迪:http://irip.buaa.edu.cn/~dihuang/ 3D人脸识别

(175)合肥高校助理助教郑伟诗:http://sist.sysu.edu.cn/~zhwshi/ 人脸识别、特征匹配、聚类、检索;

(176)google瑞士联邦卢森堡市的工程师托马斯(Thomas)Deselaers: http://thomas.deselaers.de/index.html 图像检索

(177)百度深度学习探究为主大学生后余轶南:http://www.cbsr.ia.ac.cn/users/ynyu/index.htm 目的检测,图像检索

(178)威兹曼体育大学超分辨率:http://www.wisdom.weizmann.ac.il/~vision/SingleImageSR.html

(179)伊利诺伊大学奥斯汀(Austen)分校Al
Bovik讲师:http://live.ece.utexas.edu/people/bovik/ 图像录像质地判别、特征提取

(180)以色列希伯来大学Yair
Weiss助教:http://www.cs.huji.ac.il/~yweiss/ 机器学习、超分辨率

(181)以色列希伯来高校Daniel
卓拉(Zora)n硕士:http://www.cs.huji.ac.il/~daniez/ 超分辨率、去噪

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

(183)中科院总结所副研商员常虹:http://www.jdl.ac.cn/user/hchang/index.html 图像检索、半督查学习、超分辨率

(184)以色列威茨曼大学Anat
Levin教师:http://www.wisdom.weizmann.ac.il/~levina/ 去噪、去模糊

(185)以色列威茨曼高校Daniel
Glasner大学生后:http://www.wisdom.weizmann.ac.il/~glasner/ 超分辨率、分割、姿态估量

(186)密西根高校助理讲师Honglak
Lee: http://web.eecs.umich.edu/~honglak/ 机器学习、特征提取,去噪、稀疏表示;

(187)MIT周博磊学士:http://people.csail.mit.edu/bzhou/ 聚集分析、运动检测

(188)美利坚合众国罗德岛大学Li
He大学生: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、去噪、去模糊

(190)Deep
Learning主页:http://deeplearning.net/ 深度学习杂谈、软件,代码,demo,数据等;

(191)威斯康星麦迪逊分校高校AndrewNg助教: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)微软钻探员Ce
Liu: http://people.csail.mit.edu/celiu/ 去噪、超分辨率、去模糊、分割

(194)韦斯特(West) 维吉妮亚高校助理讲师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、分割等有关代码集合

(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)北大大学程明明大学生:http://mmcheng.net/ 图像分割、检索

(198)科罗拉多理工布鲁克斯大学PhilipH.S.Torr教师:http://cms.brookes.ac.uk/staff/PhilipTorr/ 分割、三维重建

(199)堪萨斯财经大学詹姆士(James)M.Rehg教师:http://www.cc.gatech.edu/~rehg/ 分割、行人检测、特征描述、

(200)大规模图像分类、检测比赛ILSVRC(Stanford, Google举行):

 http://www.image-net.org/challenges/LSVRC/2013/

(201)加州大学尔湾分校Deva
Ramanan助理教师: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)U.S.西交大学硕士Ming
Yang: http://www.ece.northwestern.edu/~mya671/ 人脸识别、图像检索;

(204)美利哥加州高校伯克利(Berkeley)(Berkeley)分校硕士后Ross
B.Girshick:http://www.cs.berkeley.edu/~rbg/ 目的检测(DPM)

(205)中文语言资源联盟:http://www.chineseldc.org/index.html
 内有那个言语识别、字符识此外磨练,测试库;

(206)西班牙巴塞罗这大学总计机视觉中央:http://www.cvc.uab.es/adas/site/
检测、跟踪、3D、行人检测、汽车帮忙驾驶

(207)德意志联邦共和国戴姆勒探究所Prof. Dr. Dariu M.
Gavrila:http://www.gavrila.net/index.html 跟踪、行人检测、

(208)华盛顿(华盛顿)联邦财经大学安德莉亚(Andrea)s
Ess硕士后: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)山西金融学院袁飞牛讲师:http://sit.jxufe.cn/grbk/yfn/index.html\#
 烟雾检测、3D重建、教育学图像处理

(211)郑州大学Raanan Fattal讲师:http://www.cs.huji.ac.il/~raananf/
 图像增强、

(212)墨西卡利大学Dani Lischnski讲师: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:

二、图像分割Image Segmentation:

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、明显性检测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:

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、目的、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 格雷 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、人手及手指检测与识别:

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

  • Hand and Finger Detection using
    JavaCV[Project]

  • Hand and fingers
    detection[Code]

十五、场景解释:

  • Nonparametric Scene Parsing via Label Transfer
    [Project]

十六、光流Optical flow:

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

  • Dense Trajectories Video Description
    [Project]

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

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

  • Tracking Cars Using Optical
    Flow[Project]

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

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

  • Optical Flow
    Computation[Project]

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

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

  • optical flow
    relative[Project]

  • Robust Optical Flow Estimation
    [Project]

  • optical
    flow[Project]

十七、图像检索Image Retrieval:

十八、马尔科夫随机场马克(Mark)ov Random Fields:

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.htmlhttp://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

会聚不完善,欢迎补全!!更多,请关注http://blog.csdn.net/tiandijun/

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