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計算機視覺方面博客及代碼

04-13

每個做過或者正在做研究工作的人都會關注一些自己認為有價值的、活躍的研究組和個人的主頁，關注他們的主頁有時候比盲目的去搜索一些論文有用多了，大牛的或者活躍的研究者主頁往往提供了他們的最新研究線索，順便還可八一下各位大牛的經歷，對於我這樣的小菜鳥來說最最實惠的是有時可以找到源碼，很多時候光看論文是理不清思路的。

1 牛人Homepages（隨意排序，不分先後）：

1.USC Computer Vision Group：南加大，多目標跟蹤/檢測等；

2.ETHZ Computer Vision Laboratory：蘇黎世聯邦理工學院，歐洲最好的幾個CV/ML研究機構；

3.Helmut Grabner：Online Boosting and Vision的作者，tracking by online feature selection的早期經典，貌似現在不是很活躍了，跑去創業了；

4.Robert T. Collins：PSU，也是跟蹤界的大牛；

5.Ying Wu：美國西北大學，華人學者中的翹楚；

6.Junsong Yuan：NTU，上面Wu老師的學生；

7.James W. Davis：俄亥俄州立，視頻監控；

8. The Australian Centre for Visual Technologies：阿德萊德大學的CV組，最近也是exceedingly active & fruitful；

9.Chunhua Shen：屬上面的ACVT組，最近非常活躍；

10.Xi Li：同屬ACVT，之前是中科院的PHD，跟蹤方面的論文很多，有理論深度；

11.Haibin Ling：天普大學，L1-Tracker及後續擴展，源碼分享；

12.Learning, Recognition, and Surveillance：奧地利 TU Graz，在線學習，跟蹤/檢測等，active！源碼分享；

13.Statistical Visual Computing Laboratory：UCSD，光聽名字就很學術吧，Saliency研究很有名；

14.David Ross：多倫多大學，IVT的作者，跟蹤中Generative表觀的經典中的經典，提供源碼，IVT的代碼結構被後來很多人引用，值得一讀；

15.EPFL, Computer Vision Laboratory：洛桑理工的學院，和上面的的ETHZ CV lab同樣是歐洲最好的CV研究大組；

16.Jamie Shotton：屬微軟劍橋研究中心，Decision/Regression Forests；

17.Sinisa Todorovic：俄勒岡州立，行為分析等；

18.Shi Jianbo：大名鼎鼎的Good Feature to Track作者，目前方向行為分析和多目標跟蹤等；

19.Shai Avidan：特拉維夫大學，大牛級，可算是Tracking-by-detection的開創者，Ensemble Tracking, SVM Tracking；

20.Visual Information Processing and Learning：中科院計算所，山世光老師的研究組，不需介紹了吧；

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

22.Yang Jian：南京理工大學，2DPCA，人臉識別；

23.CALVIN：weakly supervised learning，objectness；

24.Learning & Vision Group：NUS，稀疏表示；

26.Xiaogang Wang：CUHK，active & fruitful，行人檢測，群體行為分析；

27.Zhou, Bolei：上面Wang老師碩士研究生，群體行為，看看人家的Publications已經輕鬆甩國內博士好幾條街；

28.Computational Vision Group：Leader--Deva Ramanan；

29.Zhang Lei：香港理工，稀疏表示，人臉識別，可以算大中華區比較活躍的研究組了，幾乎每篇論文都有對應源碼；

30.Zhang Kaihua：上面Zhang老師學生，Compressive Tracking；

31.Pramod Sharma：離線訓練檢測器的在線自適應，貌似是個不錯的topic；

32.Loris Bazzani：person re-id，他的SDALF(code)描述子經常被用來做為比較對象，說明還是有參考價值的；

33.Pedro Felzenszwalb：布朗大學，目標檢測，新新N人一枚；

34.Vijayakumar Bhagavatula：IEEE Fellow， correlation filters；

35.Laurens van der Maaten：MLer.

牛人主頁（主頁有很多論文代碼）

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.

大神們：

William T. Freeman at MIT

Roberto Cipolla at Cambridge

David Lowe at Univ. of British Columbia

Mubarak Shah at Univ. of Central Florida

Yi Ma at MSRA

Tinne Tuytelaars at K.U. Leuven

Trevor Darrell at U.C. Berkeley

Michael J. Black at Brown Univ.

重要研究組：

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

a href="http://personalrobotics.ri.cmu.edu">Personal Robotics Lab at CMU

Visual Perception Lab at Purdue Univ.

潛力牛人：

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 個人、研究機構鏈接

（1）googleResearch； research.google.com/ind

（2）MIT博士，湯曉歐學生林達華；people.csail.mit.edu/dh

（3）MIT博士後Douglas Lanman； web.media.mit.edu/~dlan

（4）opencv中文網站；opencv.org.cn/index.php

（5）Stanford大學vision實驗室； vision.stanford.edu/res

（6）Stanford大學博士崔靖宇； stanford.edu/~jycui/

（7）UCLA教授朱松純； stat.ucla.edu/~sczhu/

（8）中國人工智慧網； chinaai.org/

（9）中國視覺網； china-vision.net/

（10）中科院自動化所； ia.cas.cn/

（11）中科院自動化所李子青研究員； cbsr.ia.ac.cn/users/szl

（12）中科院計算所山世光研究員； jdl.ac.cn/user/sgshan/

（13）人臉識別主頁； face-rec.org/

（14）加州大學伯克利分校CV小組；eecs.berkeley.edu/Resea

（15）南加州大學CV實驗室； iris.usc.edu/USC-Comput

（16）卡內基梅隆大學CV主頁；

cs.cmu.edu/afs/cs/proje

（17）微軟CV研究員Richard Szeliski；research.microsoft.com/

（18）微軟亞洲研究院計算機視覺研究組； research.microsoft.com/

（19）微軟劍橋研究院ML與CV研究組； research.microsoft.com/

（20）研學論壇； bbs.matwav.com/

（21）美國Rutgers大學助理教授劉青山；research.rutgers.edu/~q

（22）計算機視覺最新資訊網； cvchina.info/

（23）運動檢測、陰影、跟蹤的測試視頻下載；apps.hi.baidu.com/share

（24）香港中文大學助理教授王曉剛； ee.cuhk.edu.hk/~xgwang/

(25)香港中文大學多媒體實驗室（湯曉鷗）; mmlab.ie.cuhk.edu.hk/

(26)U.C. San Diego. computer vision;vision.ucsd.edu/content

(27)CVonline; homepages.inf.ed.ac.uk/

(28)computer vision software; peipa.essex.ac.uk/info/

(29)Computer Vision Resource; cvpapers.com/

(30)computer vision research groups;peipa.essex.ac.uk/info/

(31)computer vision center; computervisioncentral.com

(32)浙江大學圖像技術研究與應用（ITRA）團隊：dvzju.com/

(33)自動識別網：autoid-china.com.cn/

(34)清華大學章毓晉教授：tsinghua.edu.cn/publish

(35)頂級民用機器人研究小組Porf.Gary領導的Willow Garage:willowgarage.com/

(36)上海交通大學圖像處理與模式識別研究所：pami.sjtu.edu.cn/

(37)上海交通大學計算機視覺實驗室劉允才教授：visionlab.sjtu.edu.cn/

(38)德克薩斯州大學奧斯汀分校助理教授Kristen Grauman ：cs.utexas.edu/~grauman/ 圖像分解，檢索

(39)清華大學電子工程系智能圖文信息處理實驗室（丁曉青教授）：ocrserv.ee.tsinghua.edu.cn

(40)北京大學高文教授：jdl.ac.cn/htm-gaowen/

(41)清華大學艾海舟教授：media.cs.tsinghua.edu.cn

(42)中科院生物識別與安全技術研究中心：cbsr.ia.ac.cn/china/ind

(43)瑞士巴塞爾大學 Thomas Vetter教授：informatik.unibas.ch/pe

(44)俄勒岡州立大學 Rob Hess博士：blogs.oregonstate.edu/h

(45)深圳大學 於仕祺副教授：yushiqi.cn/

(46)西安交通大學人工智慧與機器人研究所：aiar.xjtu.edu.cn/

(47)卡內基梅隆大學研究員Robert T. Collins:cs.cmu.edu/~rcollins/ho

(48)MIT博士Chris Stauffer:people.csail.mit.edu/st

(49)美國密歇根州立大學生物識別研究組(Anil K. Jain教授)：cse.msu.edu/rgroups/bio

(50)美國伊利諾伊州立大學Thomas S. Huang:beckman.illinois.edu/di

(51)武漢大學數字攝影測量與計算機視覺研究中心：whudpcv.cn/index.asp

(52)瑞士巴塞爾大學Sami Romdhani助理研究員：informatik.unibas.ch/pe

(53)CMU大學研究員Yang Wang:cs.cmu.edu/~wangy/home.

(54)英國曼徹斯特大學Tim Cootes教授：personalpages.manchester.ac.uk

(55)美國羅徹斯特大學教授Jiebo Luo:cs.rochester.edu/u/jluo

(56)美國普渡大學機器人視覺實驗室：engineering.purdue.edu/

(57)美國賓利州立大學感知、運動與認識實驗室：vision.cse.psu.edu/home

(58)美國賓夕法尼亞大學GRASP實驗室：grasp.upenn.edu/

(59)美國內達華大學裡諾校區CV實驗室：cse.unr.edu/CVL/index.p

(60)美國密西根大學vision實驗室：eecs.umich.edu/vision/i

(61)University of Massachusetts(麻省大學),視覺實驗室：vis-www.cs.umass.edu/in

(62)華盛頓大學博士後Iva Kemelmacher:cs.washington.edu/homes

(63)以色列魏茨曼科技大學Ronen Basri:wisdom.weizmann.ac.il/~

(64)瑞士ETH-Zurich大學CV實驗室：vision.ee.ethz.ch/boost

(65)微軟CV研究員張正友：research.microsoft.com/

(66)中科院自動化所醫學影像研究室：3dmed.net/

(67)中科院田捷研究員：3dmed.net/tian/

(68)微軟Redmond研究院研究員Simon Baker:research.microsoft.com/

(69)普林斯頓大學教授李凱：cs.princeton.edu/~li/

(70)普林斯頓大學博士賈登：cs.princeton.edu/~jiade

(71)牛津大學教授Andrew Zisserman： robots.ox.ac.uk/~az/

(72)英國leeds大學研究員Mark Everingham:comp.leeds.ac.uk/me/

(73)英國愛丁堡大學教授Chris William: homepages.inf.ed.ac.uk/

(74)微軟劍橋研究院研究員John Winn: johnwinn.org/

(75)喬治亞理工學院教授Monson H.Hayes：savannah.gatech.edu/peo

(76)微軟亞洲研究院研究員孫劍：research.microsoft.com/

(77)微軟亞洲研究院研究員馬毅：research.microsoft.com/

(78)英國哥倫比亞大學教授David Lowe: cs.ubc.ca/~lowe/

(79)英國愛丁堡大學教授Bob Fisher: homepages.inf.ed.ac.uk/

(80)加州大學聖地亞哥分校教授Serge J.Belongie:cseweb.ucsd.edu/~sjb/

(81)威斯康星大學教授Charles R.Dyer: pages.cs.wisc.edu/~dyer

(82)多倫多大學教授Allan.Jepson: cs.toronto.edu/~jepson/

(83)倫斯勒理工學院教授Qiang Ji: ecse.rpi.edu/~qji/

(84)CMU研究員Daniel Huber: ri.cmu.edu/person.html?

(85)多倫多大學教授：David J.Fleet: cs.toronto.edu/~fleet/

(86)倫敦大學瑪麗女王學院教授Andrea Cavallaro:eecs.qmul.ac.uk/~andrea

(87)多倫多大學教授Kyros Kutulakos: cs.toronto.edu/~kyros/

(88)杜克大學教授Carlo Tomasi: cs.duke.edu/~tomasi/

(89)CMU教授Martial Hebert: cs.cmu.edu/~hebert/

(90)MIT助理教授Antonio Torralba: web.mit.edu/torralba/ww

(91)馬里蘭大學研究員Yasel Yacoob: umiacs.umd.edu/users/ya

(92)康奈爾大學教授Ramin Zabih: cs.cornell.edu/~rdz/

(93)CMU博士田淵棟: cs.cmu.edu/~yuandong/

(94)CMU副教授Srinivasa Narasimhan: cs.cmu.edu/~srinivas/

(95)CMU大學ILIM實驗室：cs.cmu.edu/~ILIM/

(96)哥倫比亞大學教授Sheer K.Nayar: cs.columbia.edu/~nayar/

(97)三菱電子研究院研究員Fatih Porikli ：porikli.com/

(98)康奈爾大學教授Daniel Huttenlocher：cs.cornell.edu/~dph/

(99)南京大學教授周志華：cs.nju.edu.cn/zhouzh/in

(100)芝加哥豐田技術研究所助理教授Devi Parikh: ttic.uchicago.edu/~dpar

(101)瑞士聯邦理工學院博士後Helmut Grabner:vision.ee.ethz.ch/~hegr

(102)香港中文大學教授賈佳亞：cse.cuhk.edu.hk/~leojia

(103)南京大學教授吳建鑫：c2inet.sce.ntu.edu.sg/J

(104)GE研究院研究員李關：cs.unc.edu/~lguan/

(105)喬治亞理工學院教授Monson Hayes:savannah.gatech.edu/peo

(106)圖片檢索國際競賽PASCAL VOC(微軟劍橋研究院組織):pascallin.ecs.soton.ac.uk

(107)機器視覺開源處理庫匯總：archive.cnblogs.com/a/2

(108)布朗大學教授Benjamin Kimia: lems.brown.edu/kimia.ht

(109)數據堂-圖像處理相關的樣本數據：datatang.com/data/list/

(110)東軟基於CV的汽車輔助駕駛系統：neusoft.com/cn/solution

(111)馬里蘭大學教授Rema Chellappa:cfar.umd.edu/~rama/

(112)芝加哥豐田研究中心助理教授Devi Parikh：ttic.uchicago.edu/~dpar

(113)賓夕法尼亞大學助理教授石建波：cis.upenn.edu/~jshi/

(114)比利時魯汶大學教授Luc Van Gool：vision.ee.ethz.ch/membe, vision.ee.ethz.ch/~vang

(115)行人檢測主頁：pedestrian-detection.com

(116)法國學習演算法與系統實驗室Basilio Noris博士：lasa.epfl.ch/people/mem mldemos.epfl.ch/

(117)美國馬里蘭大學LARRY S.DAVIS教授：umiacs.umd.edu/~lsd/

(118)計算機視覺論文分類導航：visionbib.com/bibliogra

(119)計算機視覺分類信息導航：visionbib.com/

(120)西班牙馬德里理工大學博士Marcos Nieto：marcosnieto.net/

(121)香港理工大學副教授張磊：www4.comp.polyu.edu.hk/

(122)以色列技術學院教授Michael Elad：cs.technion.ac.il/~elad

(123)韓國啟明大學計算機視覺與模式識別實驗室：cvpr.kmu.ac.kr/

(124)英國諾丁漢大學Michel Valstar博士：cs.nott.ac.uk/~mfv/

(125)卡內基梅隆大學Takeo Kanade教授:ri.cmu.edu/people/kanad

(126)微軟學術搜索：libra.msra.cn/

(127)比利時天主教魯汶大學Radu Timofte博士：homes.esat.kuleuven.be/，交通標誌檢測，定位，3D跟蹤

(128)迪斯尼匹茲堡研究院研究員：Iain Matthews:iainm.com/iainm/Home.ht

ri.cmu.edu/person.html? AAM,三維重建

（129）康奈爾大學視覺與圖像分析組：via.cornell.edu/ 醫學圖像處理

（130）密西根州立大學生物識別研究組：cse.msu.edu/biometrics/ 人臉識別、指紋識別、圖像檢索

（131）柏林科技大學計算機視覺與遙感實驗室：cv.tu-berlin.de/menue/c 圖像分析、物體重建、基於圖像的表面測量、醫學圖像處理

（132）英國布里斯託大學數字多媒體研究組：cs.bris.ac.uk/Research/ 運動檢測與跟蹤、視頻壓縮、3D重建、字元定位

（133）英國薩利大學視覺、語音與信號處理中心： surrey.ac.uk/cvssp/ 人臉識別、監控、3D、視頻檢索、

（134）北卡萊羅納大學教堂山分校Marc Pollefeys教授：cs.unc.edu/~marc/ 基於視頻的3D模型生成、相機標定、運動檢測與分析、3D重建

（135）澳大利亞國立大學Richard Hartley教授：users.cecs.anu.edu.au/~ 運動估計、稀疏子空間、跟蹤、

（136）百度技術副總監於凱：dbs.ifi.lmu.de/~yu_k/ 深度學習，稀疏表示，圖像分類

（137）西安電子科技大學高新波教授：web.xidian.edu.cn/xbgao 質量評判、水印、稀疏表示、超解析度

（138）加州大學伯克利分校Michael I.Jordan教授：cs.berkeley.edu/~jordan 機器學習

（139）加州理工行人檢測相關資料：vision.caltech.edu/Imag

（140）微軟Redmond研究院研究員Piotr Dollar: vision.ucsd.edu/~pdolla 行人檢測、特徵提取、

（141）視覺計算研究論壇：sigvc.org/bbs/ 中科院視覺計算研究小組的論壇

（142）美國坦尚尼亞州立大學稀疏學習軟體包：public.asu.edu/~jye02/S 稀疏學習

（143）美國加州大學聖地亞哥分校Jacob Whitehill博士：mplab.ucsd.edu/~jake/ 機器學習

（144）美國布朗大學Michael J.Black教授：cs.brown.edu/~black/ 人的姿態估計和跟蹤

（145）美國加州大學聖地亞哥分校David Kriegman教授：cseweb.ucsd.edu/~kriegm 人臉識別

（146）南加州大學Paul Debevec教授：ict.debevec.org/~debeve 或 pauldebevec.com/ 將CV和CG結合研究 人臉捕捉重建技術

（147）伊利諾伊大學D.A.Forsyth教授：luthuli.cs.uiuc.edu/~da 三維重建

（148）英國牛津大學Ian Reid教授：robots.ox.ac.uk/~ian/ 跟蹤和機器人導航

（149）CMU大學Alyosha Efros 教授: cs.cmu.edu/~efros/ 圖像紋理合成

（150）加州大學伯克利分校Jitendra Malik教授：cs.berkeley.edu/~malik/ 輪廓檢測、圖像/視頻分割、圖形匹配、目標識別

（151）MIT教授William Freeman： people.csail.mit.edu/bi 圖像紋理合成

（152）CMU博士Henry Schneiderman： cs.cmu.edu/~hws/ 目標檢測和識別；

（153）微軟研究員Paul Viola: research.microsoft.com/ AdaBoost演算法

（154）微軟研究員Antonio Criminisi: research.microsoft.com/ 圖像修補，三維重建，目標檢測與跟蹤；

（155）魏茨曼科學研究所教授Michal Irani: wisdom.weizmann.ac.il/~ 超解析度

（156）瑞士洛桑理工學院Pascal Fua教授：people.epfl.ch/pascal.f 立體視覺，增強現實

（157）喬治亞理工學院Irfan Essa教授：ic.gatech.edu/people/ir 人臉表情識別

（158）中科院助理教授樊彬：sigvc.org/bfan/ 特徵描述；

（159）斯坦福大學Sebastian Thrun教授：robots.stanford.edu/ind 機器人；

（160）多倫多大學Geoffrey E.Hinton教授：cs.toronto.edu/~hinton/ 深度學習

（161）鳳巢系統架構師張棟博士：weibo.com/machinelearni

（162）2012年龍星計劃機器學習課程：bigeye.au.tsinghua.edu.cn

（163）中科院自動化所肖柏華教授：compsys.ia.ac.cn/people 文字識別、人臉識別、質量評判

（164）圖像視頻質量評判：live.ece.utexas.edu/res

（165）紐約大學Yann LeCun教授yann.lecun.com/ yann.lecun.com/exdb/mni 手寫體數字識別

（166）二維條碼識別開源庫zxing：code.google.com/p/zxing

（167）布朗大學Pedro Felzenszwalb教授：cs.brown.edu/~pff/ 特徵提取，Deformable Part Model

（168）伊利諾伊香檳大學Svetlana Lazebnik教授：cs.illinois.edu/homes/s 特徵提取，聚類，圖像檢索

（169）荷蘭烏德勒支大學圖像與多媒體研究中心cs.uu.nl/centers/give/m 圖像、多媒體檢索與匹配

（170）英國格拉斯哥大學信息檢索小組：ir.dcs.gla.ac.uk/ 文本、圖像、視頻檢索

（171）中科院自動化所孫哲南助理教書：cbsr.ia.ac.cn/users/zns 虹膜識別、掌紋識別、人臉識別

（172）南京信息工程大學劉青山教授：jstuoke.com/web/xky/det 人臉圖像分析、醫學圖像分析

（173）清華大學助理教授馮建江：ivg.au.tsinghua.edu.cn/ 指紋識別

（174）北航助理教授黃迪：irip.buaa.edu.cn/~dihua 3D人臉識別

（175）中山大學助理教授鄭偉詩：sist.sysu.edu.cn/~zhwsh 人臉識別、特徵匹配、聚類、檢索；

（176）google瑞士蘇黎世的工程師Thomas Deselaers: thomas.deselaers.de/ind 圖像檢索

（177）百度深度學習研究中心博士後余軼南：cbsr.ia.ac.cn/users/yny 目標檢測，圖像檢索

（178）威茲曼科技大學超解析度：wisdom.weizmann.ac.il/~

（179）德克薩斯大學奧斯汀分校Al Bovik教授：live.ece.utexas.edu/peo 圖像視頻質量判別、特徵提取

（180）以色列希伯來大學Yair Weiss教授：cs.huji.ac.il/~yweiss/ 機器學習、超解析度

（181）以色列希伯來大學Daniel Zoran博士：cs.huji.ac.il/~daniez/ 超解析度、去噪

（182）美國加州大學Peyman Milanfar教授：users.soe.ucsc.edu/~mil 去噪

（183）中科院計算所副研究員常虹：jdl.ac.cn/user/hchang/i 圖像檢索、半監督學習、超解析度

（184）以色列威茨曼大學Anat Levin教授：wisdom.weizmann.ac.il/~ 去噪、去模糊

（185）以色列威茨曼大學Daniel Glasner博士後：wisdom.weizmann.ac.il/~ 超解析度、分割、姿態估計

（186）密西根大學助理教授Honglak Lee: web.eecs.umich.edu/~hon 機器學習、特徵提取，去噪、稀疏表示；

（187）MIT周博磊博士：people.csail.mit.edu/bz 聚集分析、運動檢測

（188）美國田納西大學Li He博士：web.eecs.utk.edu/~lhe4/ 稀疏表示、超解析度；

（189）Adobe研究院Jianchao Yang研究員：ifp.illinois.edu/~jyang 稀疏表示，超解析度、圖片檢索、去噪、去模糊

（190）Deep Learning主頁：deeplearning.net/ 深度學習論文、軟體，代碼，demo，數據等；

（191）斯坦福大學Andrew Ng教授：cs.stanford.edu/people/ 深度神經網路，深度學習

（192）Elefant: elefant.developer.nicta.com.au 機器學習開源庫

（193）微軟研究員Ce Liu: people.csail.mit.edu/ce 去噪、超解析度、去模糊、分割

（194）West Virginia大學助理教授Xin Li: csee.wvu.edu/~xinl/ 邊緣檢測、降噪、去模糊

（195）csee.wvu.edu/~xinl/sour 深度學習、去噪、編碼、壓縮感知、超解析度、聚類、分割等相關代碼集合

（196）西班牙格拉納達大學超解析度重建項目組：decsai.ugr.es/pi/superr

（197）清華大學程明明博士：mmcheng.net/ 圖像分割、檢索

（198）牛津布魯克斯大學Philip H.S.Torr教授：cms.brookes.ac.uk/staff 分割、三維重建

（199）喬治亞理工學院James M.Rehg教授：cc.gatech.edu/~rehg/ 分割、行人檢測、特徵描述、

（200）大規模圖像分類、檢測競賽ILSVRC（Stanford, Google舉辦）:

image-net.org/challenge

（201）加州大學爾灣分校Deva Ramanan助理教授：ics.uci.edu/~dramanan/ 目標檢測，行人檢測，跟蹤、稀疏表示

（202）人臉識別測試圖片集：mlcv.net/

（203）美國西北大學博士Ming Yang: ece.northwestern.edu/~m 人臉識別、圖像檢索；

（204）美國加州大學伯克利分校博士後Ross B.Girshick：cs.berkeley.edu/~rbg/ 目標檢測（DPM）

（205）中文語言資源聯盟：chineseldc.org/index.ht 內有很多語言識別、字元識別的訓練，測試庫；

（206）西班牙巴塞羅那大學計算機視覺中心：cvc.uab.es/adas/site/ 檢測、跟蹤、3D、行人檢測、汽車輔助駕駛

（207）德國戴姆勒研究所Prof. Dr. Dariu M. Gavrila：gavrila.net/index.html 跟蹤、行人檢測、

（208）蘇黎世聯邦理工學院Andreas Ess博士後：vision.ee.ethz.ch/~aess 行人檢測、行為檢測、跟蹤

（209）Libqrencode: fukuchi.org/works/qrenc 基於C語言的QR二維碼編碼開源庫

（210）江西財經大學袁飛牛教授：sit.jxufe.cn/grbk/yfn/i 煙霧檢測、3D重建、醫學圖像處理

（211）耶路撒冷大學Raanan Fattal教師：cs.huji.ac.il/~raananf/ 圖像增強、

（212）耶路撒冷大學Dani Lischnski教授：cs.huji.ac.il/~danix/ 去模糊、紋理合成、圖像增強

3 代碼匯總

一、特徵提取Feature Extraction：

  • SIFT [1] [Demo program][SIFT Library] [VLFeat]
  • PCA-SIFT [2] [Project]
  • Affine-SIFT [3] [Project]
  • SURF [4] [OpenSURF] [Matlab Wrapper]
  • Affine Covariant Features [5] [Oxford project]
  • MSER [6] [Oxford project] [VLFeat]
  • Geometric Blur [7] [Code]
  • Local Self-Similarity Descriptor [8] [Oxford implementation]
  • Global and Efficient Self-Similarity [9] [Code]
  • Histogram of Oriented Graidents [10] [INRIA Object Localization Toolkit] [OLT toolkit for Windows]
  • GIST [11] [Project]
  • Shape Context [12] [Project]
  • Color Descriptor [13] [Project]
  • Pyramids of Histograms of Oriented Gradients [Code]
  • Space-Time Interest Points (STIP) [14][Project] [Code]
  • Boundary Preserving Dense Local Regions [15][Project]
  • Weighted Histogram[Code]
  • Histogram-based Interest Points Detectors[Paper][Code]
  • An OpenCV - C++ implementation of Local Self Similarity Descriptors [Project]
  • Fast Sparse Representation with Prototypes[Project]
  • Corner Detection [Project]
  • AGAST Corner Detector: faster than FAST and even FAST-ER[Project]
  • Real-time Facial Feature Detection using Conditional Regression Forests[Project]
  • Global and Efficient Self-Similarity for Object Classification and Detection[code]
  • WαSH: Weighted α-Shapes for Local Feature Detection[Project]
  • HOG[Project]
  • Online Selection of Discriminative Tracking Features[Project]

二、圖像分割Image Segmentation：

  • Normalized Cut [1] [Matlab code]
  • Gerg Mori』 Superpixel code [2] [Matlab code]
  • Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]
  • Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper]
  • OWT-UCM Hierarchical Segmentation [5] [Resources]
  • Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code]
  • Quick-Shift [7] [VLFeat]
  • SLIC Superpixels [8] [Project]
  • Segmentation by Minimum Code Length [9] [Project]
  • Biased Normalized Cut [10] [Project]
  • Segmentation Tree [11-12] [Project]
  • Entropy Rate Superpixel Segmentation [13] [Code]
  • Fast Approximate Energy Minimization via Graph Cuts[Paper][Code]
  • Ef?cient Planar Graph Cuts with Applications in Computer Vision[Paper][Code]
  • Isoperimetric Graph Partitioning for Image Segmentation[Paper][Code]
  • Random Walks for Image Segmentation[Paper][Code]
  • Blossom V: A new implementation of a minimum cost perfect matching algorithm[Code]
  • An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision[Paper][Code]
  • Geodesic Star Convexity for Interactive Image Segmentation[Project]
  • Contour Detection and Image Segmentation Resources[Project][Code]
  • Biased Normalized Cuts[Project]
  • Max-flow/min-cut[Project]
  • Chan-Vese Segmentation using Level Set[Project]
  • A Toolbox of Level Set Methods[Project]
  • Re-initialization Free Level Set Evolution via Reaction Diffusion[Project]
  • Improved C-V active contour model[Paper][Code]
  • A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction[Paper][Code]
  • Level Set Method Research by Chunming Li[Project]
  • ClassCut for Unsupervised Class Segmentation[code]
  • SEEDS: Superpixels Extracted via Energy-Driven Sampling [Project][other]

三、目標檢測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]
  • Wheres 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、顯著性檢測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、圖像分類、聚類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、摳圖Image Matting

  • A Closed Form Solution to Natural Image Matting [Code]
  • Spectral Matting [Project]
  • Learning-based Matting [Code]

七、目標跟蹤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 手指跟蹤[code]

九、3D相關：

  • 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、機器學習演算法：

  • 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 (余凱)[Video]
  • CNN - Convolutional neural network class[Matlab Tool]
  • Yann LeCuns 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. Hintons 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、目標、行為識別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、圖像處理：

  • Distance Transforms of Sampled Functions[Project]
  • The Computer Vision Homepage[Project]
  • Efficient appearance distances between windows[code]
  • Image Exploration algorithm[code]
  • Motion Magnification 運動放大 [Project]
  • Bilateral Filtering for Gray and Color Images 雙邊濾波器 [Project]
  • A Fast Approximation of the Bilateral Filter using a Signal Processing Approach [

十三、一些實用工具：

  • EGT: a Toolbox for Multiple View Geometry and Visual Servoing[Project] [Code]
  • a development kit of matlab mex functions for OpenCV library[Project]
  • Fast Artificial Neural Network Library[Project]

十四、人手及指尖檢測與識別：

  • finger-detection-and-gesture-recognition [Code]
  • Hand and Finger Detection using JavaCV[Project]
  • Hand and fingers detection[Code]

十五、場景解釋：

  • 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、圖像檢索Image Retrieval：

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

十八、馬爾科夫隨機場Markov Random Fields：

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

十九、運動檢測Motion detection：

  • Moving Object Extraction, Using Models or Analysis of Regions [Project]
  • Background Subtraction: Experiments and Improvements for ViBe [Project]
  • A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications [Project]
  • changedetection.net: A new change detection benchmark dataset[Project]
  • ViBe - a powerful technique for background detection and subtraction in video sequences[Project]
  • Background Subtraction Program[Project]
  • Motion Detection Algorithms[Project]
  • Stuttgart Artificial Background Subtraction Dataset[Project]
  • Object Detection, Motion Estimation, and Tracking[Project]

Feature Detection and Description

General Libraries:

    • VLFeat – Implementation of various feature descriptors (including SIFT, HOG, and LBP) and covariant feature detectors (including DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris). Easy-to-use Matlab interface. See Modern features: Software – Slides providing a demonstration of VLFeat and also links to other software. Check also VLFeat hands-on session training
    • OpenCV – Various implementations of modern feature detectors and descriptors (SIFT, SURF, FAST, BRIEF, ORB, FREAK, etc.)

Fast Keypoint Detectors for Real-time Applications:

    • FAST – High-speed corner detector implementation for a wide variety of platforms
    • AGAST – Even faster than the FAST corner detector. A multi-scale version of this method is used for the BRISK descriptor (ECCV 2010).

Binary Descriptors for Real-Time Applications:

    • BRIEF – C++ code for a fast and accurate interest point descriptor (not invariant to rotations and scale) (ECCV 2010)
    • ORB – OpenCV implementation of the Oriented-Brief (ORB) descriptor (invariant to rotations, but not scale)
    • BRISK – Efficient Binary descriptor invariant to rotations and scale. It includes a Matlab mex interface. (ICCV 2011)
    • FREAK – Faster than BRISK (invariant to rotations and scale) (CVPR 2012)

SIFT and SURF Implementations:

    • SIFT: VLFeat, OpenCV, Original code by David Lowe, GPU implementation, OpenSIFT
    • SURF: Herbert Bay』s code, OpenCV, GPU-SURF

Other Local Feature Detectors and Descriptors:

    • VGG Affine Covariant features – Oxford code for various affine covariant feature detectors and descriptors.
    • LIOP descriptor – Source code for the Local Intensity order Pattern (LIOP) descriptor (ICCV 2011).
    • Local Symmetry Features – Source code for matching of local symmetry features under large variations in lighting, age, and rendering style (CVPR 2012).

Global Image Descriptors:

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

Feature Coding and Pooling

    • VGG Feature Encoding Toolkit – Source code for various state-of-the-art feature encoding methods – including Standard hard encoding, Kernel codebook encoding, Locality-constrained linear encoding, and Fisher kernel encoding.
    • Spatial Pyramid Matching – Source code for feature pooling based on spatial pyramid matching (widely used for image classification)

Convolutional Nets and Deep Learning

    • EBLearn – C++ Library for Energy-Based Learning. It includes several demos and step-by-step instructions to train classifiers based on convolutional neural networks.
    • Torch7 – Provides a matlab-like environment for state-of-the-art machine learning algorithms, including a fast implementation of convolutional neural networks.
    • Deep Learning - Various links for deep learning software.

Part-Based Models

    • Deformable Part-based Detector – Library provided by the authors of the original paper (state-of-the-art in PASCAL VOC detection task)
    • Efficient Deformable Part-Based Detector – Branch-and-Bound implementation for a deformable part-based detector.
    • Accelerated Deformable Part Model – Efficient implementation of a method that achieves the exact same performance of deformable part-based detectors but with significant acceleration (ECCV 2012).
    • Coarse-to-Fine Deformable Part Model – Fast approach for deformable object detection (CVPR 2011).
    • Poselets – C++ and Matlab versions for object detection based on poselets.
    • Part-based Face Detector and Pose Estimation – Implementation of a unified approach for face detection, pose estimation, and landmark localization (CVPR 2012).

Attributes and Semantic Features

      • Relative Attributes – Modified implementation of RankSVM to train Relative Attributes (ICCV 2011).
      • Object Bank – Implementation of object bank semantic features (NIPS 2010). See also ActionBank
      • Classemes, Picodes, and Meta-class features – Software for extracting high-level image descriptors (ECCV 2010, NIPS 2011, CVPR 2012).

Large-Scale Learning

      • Additive Kernels – Source code for fast additive kernel SVM classifiers (PAMI 2013).
      • LIBLINEAR – Library for large-scale linear SVM classification.
      • VLFeat – Implementation for Pegasos SVM and Homogeneous Kernel map.

Fast Indexing and Image Retrieval

      • FLANN – Library for performing fast approximate nearest neighbor.
      • Kernelized LSH – Source code for Kernelized Locality-Sensitive Hashing (ICCV 2009).
      • ITQ Binary codes – Code for generation of small binary codes using Iterative Quantization and other baselines such as Locality-Sensitive-Hashing (CVPR 2011).
      • INRIA Image Retrieval – Efficient code for state-of-the-art large-scale image retrieval (CVPR 2011).

Object Detection

      • See Part-based Models and Convolutional Nets above.
      • Pedestrian Detection at 100fps – Very fast and accurate pedestrian detector (CVPR 2012).
      • Caltech Pedestrian Detection Benchmark – Excellent resource for pedestrian detection, with various links for state-of-the-art implementations.
      • OpenCV – Enhanced implementation of Viola&Jones real-time object detector, with trained models for face detection.
      • Efficient Subwindow Search – Source code for branch-and-bound optimization for efficient object localization (CVPR 2008).

3D Recognition

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

Action Recognition

      • ActionBank – Source code for action recognition based on the ActionBank representation (CVPR 2012).
      • STIP Features – software for computing space-time interest point descriptors
      • Independent Subspace Analysis – Look for Stacked ISA for Videos (CVPR 2011)
      • Velocity Histories of Tracked Keypoints - C++ code for activity recognition using the velocity histories of tracked keypoints (ICCV 2009)

Datasets

Attributes

      • Animals with Attributes – 30,475 images of 50 animals classes with 6 pre-extracted feature representations for each image.
      • aYahoo and aPascal – Attribute annotations for images collected from Yahoo and Pascal VOC 2008.
      • FaceTracer – 15,000 faces annotated with 10 attributes and fiducial points.
      • PubFig – 58,797 face images of 200 people with 73 attribute classifier outputs.
      • [url=vis-www.cs.umass.edu/lfw/]LFW[/url] – 13,233 face images of 5,749 people with 73 attribute classifier outputs.
      • Human Attributes – 8,000 people with annotated attributes. Check also this link for another dataset of human attributes.
      • SUN Attribute Database – Large-scale scene attribute database with a taxonomy of 102 attributes.
      • ImageNet Attributes – Variety of attribute labels for the ImageNet dataset.
      • Relative attributes – Data for OSR and a subset of PubFig datasets. Check also this link for the WhittleSearch data.
      • Attribute Discovery Dataset – Images of shopping categories associated with textual descriptions.

Fine-grained Visual Categorization

      • Caltech-UCSD Birds Dataset – Hundreds of bird categories with annotated parts and attributes.
      • Stanford Dogs Dataset – 20,000 images of 120 breeds of dogs from around the world.
      • Oxford-IIIT Pet Dataset – 37 category pet dataset with roughly 200 images for each class. Pixel level trimap segmentation is included.
      • Leeds Butterfly Dataset – 832 images of 10 species of butterflies.
      • Oxford Flower Dataset – Hundreds of flower categories.

Face Detection

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

Face Recognition

      • Face Recognition Homepage – Large collection of face recognition datasets.
      • [url=vis-www.cs.umass.edu/lfw/]LFW[/url] – UMass unconstrained face recognition dataset (13,000+ face images).
      • NIST Face Homepage – includes face recognition grand challenge (FRGC), vendor tests (FRVT) and others.
      • CMU Multi-PIE – contains more than 750,000 images of 337 people, with 15 different views and 19 lighting conditions.
      • FERET – Classical face recognition dataset.
      • Deng Cai』s face dataset in Matlab Format – Easy to use if you want play with simple face datasets including Yale, ORL, PIE, and Extended Yale B.
      • SCFace – Low-resolution face dataset captured from surveillance cameras.

Handwritten Digits

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

Pedestrian Detection

      • Caltech Pedestrian Detection Benchmark – 10 hours of video taken from a vehicle,350K bounding boxes for about 2.3K unique pedestrians.
      • INRIA Person Dataset – Currently one of the most popular pedestrian detection datasets.
      • ETH Pedestrian Dataset – Urban dataset captured from a stereo rig mounted on a stroller.
      • TUD-Brussels Pedestrian Dataset – Dataset with image pairs recorded in an crowded urban setting with an onboard camera.
      • PASCAL Human Detection – One of 20 categories in PASCAL VOC detection challenges.
      • USC Pedestrian Dataset – Small dataset captured from surveillance cameras.

Generic Object Recognition

      • ImageNet – Currently the largest visual recognition dataset in terms of number of categories and images.
      • Tiny Images – 80 million 32x32 low resolution images.
      • Pascal VOC – One of the most influential visual recognition datasets.
      • Caltech 101 / Caltech 256 – Popular image datasets containing 101 and 256 object categories, respectively.
      • MIT LabelMe – Online annotation tool for building computer vision databases.

Scene Recognition

      • MIT SUN Dataset – MIT scene understanding dataset.
      • UIUC Fifteen Scene Categories – Dataset of 15 natural scene categories.

Feature Detection and Description

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

Action Recognition

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

RGBD Recognition

      • RGB-D Object Dataset – Dataset containing 300 common household objects

Reference:

[1]: rogerioferis.com/Visual

特徵提取

      • SURF特徵: http://www.vision.ee.ethz.ch/software/index.de.html(當然這只是其中之一)
      • LBP特徵(一種紋理特徵)：comp.hkbu.edu.hk/~icpr0
      • Fast Corner Detection（OpenCV中的Fast演算法）:FAST Corner Detection -- Edward Rosten

機器視覺

      • A simple object detector with boosting(Awarded the Best Short Course Prize at ICCV 2005，So了解adaboost的推薦之作)：people.csail.mit.edu/to
      • Boosting(該網頁上有相當全的Boosting的文章和幾個Boosting代碼，本人推薦)：cbio.mskcc.org/~aarvey/
      • Adaboost Matlab 工具：graphics.cs.msu.ru/en/s
      • MultiBoost(不說啥了，多類Adaboost演算法的程序)：sourceforge.net/project
      • TextonBoost(我們教研室王冠夫師兄的畢設): Jamie Shotton - Code
      • LibSvm的老爹（推薦）: csie.ntu.edu.tw/~cjlin/
      • Conditional Random Fields（CRF論文+Code列表，推薦）
      • CRF++: Yet Another CRF toolkit
      • Conditional Random Field (CRF) Toolbox for Matlab
      • Tree CRFs
      • LingPipe: Installation
      • Hidden Markov Models（推薦）
      • 隱馬爾科夫模型(Hidden Markov Models)系列之一 - eaglex的專欄 - 博客頻道 - CSDN.NET（推薦）

綜合代碼

      • CvPapers(好吧，牛吧網站，裡面有ICCV，CVPR，ECCV，SIGGRAPH的論文收錄，然後還有一些論文的代碼搜集，要求加精！)：cvpapers.com/
      • Computer Vision Software(裡面代碼很多，並詳細的給出了分類)：peipa.essex.ac.uk/info/
      • 某人的Windows Live（我看裡面東東不少就收藏了）：skydrive.live.com/?
      • MATLAB and Octave Functions for Computer Vision and Image Processing（這個裡面的東西也很全，只是都是用Matlab和Octave開發的）：csse.uwa.edu.au/~pk/res
      • Computer Vision Resources（裡面的視覺演算法很多，給出了相應的論文和Code，挺好的）：netfiles.uiuc.edu/jbhua
      • MATLAB Functions for Multiple View Geometry（關於物體多視角計算的庫）：robots.ox.ac.uk/~vgg/hz
      • Evolutive Algorithm based on Na?ve Bayes models Estimation（單獨列了一個演算法的Code）：cvc.uab.cat/~xbaro/eanb

主頁代碼

      • Pablo Negris Home Page
      • Jianxin Wus homepage
      • Peter Carbonetto
      • Markov Random Fields for Super-Resolution
      • Detecting and Sketching the Common
      • Pedro Felzenszwalb
      • Hae JONG, SEO
      • CAP 5416 - Computer Vision
      • Parallel Tracking and Mapping for Small AR Workspaces (PTAM)
      • Deva Ramanan - UC Irvine - Computer Vision
      • Raghuraman Gopalan
      • Hui Kong
      • Jamie Shotton - Post-Doctoral Researcher in Computer Vision
      • Jean-Yves AUDIBERT
      • Olga Veksler
      • Stephen Gould
      • Publications (Last Update: 09/30/10)
      • Karim Ali - FlowBoost
      • A simple parts and structure object detector
      • Code - Oxford Brookes Vision Group
      • Taku Kudo

行人檢測

      • Histogram of Oriented Gradient (Windows)
      • INRIA Pedestrian detector
      • Poselets
      • William Robson Schwartz - Softwares
      • calvin upper-body detector v1.02
      • RPT@CVG
      • Main Page
      • Source Code
      • Dr. Luciano Spinello
      • Pedestrian Detection
      • Class-Specific Hough Forests for Object Detection
      • Jianxin Wus homepage（就是上面的）
      • Berkeley大學做的Pedestrian Detector，使用交叉核的支持向量機，特徵使用HOG金字塔，提供Matlab和C++混編的代碼：cs.berkeley.edu/~smaji/

視覺壁障

      • High Speed Obstacle Avoidance using Monocular Vision and Reinforcement Learning
      • TLD(2010年很火的tracking演算法)
      • online boosting trackers
      • Boris Babenko
      • Optical Flow Algorithm Evaluation (提供了一個動態貝葉斯網路框架，例如遞 歸信息處理與分析、卡爾曼濾波、粒子濾波、序列蒙特卡羅方法等，C++寫的)http://of-eval.sourceforge.net/

物體檢測演算法

      • Object Detection
      • Software for object detection

人臉檢測

      • Source Code
      • 10個人臉檢測項目
      • Jianxin Wus homepage（又是這貨）

ICA獨立成分分析

      • An ICA page-papers,code,demo,links (Tony Bell)
      • FastICA
      • Cached k-d tree search for ICP algorithms

濾波演算法

      • 卡爾曼濾波：The Kalman Filter(終極網頁)
      • Bayesian Filtering Library: The Bayesian Filtering Library

路面識別

      • Source Code
      • Vanishing point detection for general road detection

分割演算法

      • MATLAB Normalized Cuts Segmentation Code：software
      • 超像素分割：SLIC Superpixels

附： blog.sina.com.cn/s/blog，yuanyong.org/cv/cv-code參考：

blog.csdn.net/carson200

blog.csdn.net/chlele010

blog.csdn.net/yihaizhiy

sigvc.org/bbs/forum.php


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