尊重原創，轉載自：點擊打開鏈接
每個做過或者正在做研究工作的人都會關注一些自己認爲有價值的、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，行爲分析等；
18.Shi Jianbo：大名鼎鼎的Good Feature to Track作者，目前方向行爲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，羣體行爲分析；
27.Zhou, Bolei：上面Wang老師碩士研究生，羣體行爲，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爲比較對象，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）
（1）googleResearch； http://research.google.com/index.html
（2）MIT博士，湯曉歐學生林達華；http://people.csail.mit.edu/dhlin/index.html
（3）MIT博士後Douglas Lanman； http://web.media.mit.edu/~dlanman/
（4）opencv中文網站；http://www.opencv.org.cn/index.php/%E9%A6%96%E9%A1%B5
（5）Stanford大學vision實驗室； http://vision.stanford.edu/research.html
（6）Stanford大學博士崔靖宇； http://www.stanford.edu/~jycui/
（7）UCLA教授朱鬆純； http://www.stat.ucla.edu/~sczhu/
（8）中國人工智能網； http://www.chinaai.org/
（9）中國視覺網； http://www.china-vision.net/
（10）中科院自動化所； http://www.ia.cas.cn/
（11）中科院自動化所李子青研究員； http://www.cbsr.ia.ac.cn/users/szli/
（12）中科院計算所山世光研究員； http://www.jdl.ac.cn/user/sgshan/
（13）人臉識別主頁； http://www.face-rec.org/
（14）加州大學伯克利分校CV小組；http://www.eecs.berkeley.edu/Research/Projects/CS/vision/
（15）南加州大學CV實驗室； http://iris.usc.edu/USC-Computer-Vision.html
（16）卡內基梅隆大學CV主頁；
http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html
（17）微軟CV研究員Richard Szeliski；http://research.microsoft.com/en-us/um/people/szeliski/
（18）微軟亞洲研究院計算機視覺研究組； http://research.microsoft.com/en-us/groups/vc/
（19）微軟劍橋研究院ML與CV研究組； http://research.microsoft.com/en-us/groups/mlp/default.aspx
（20）研學論壇； http://bbs.matwav.com/
（21）美國Rutgers大學助理教授劉青山；http://www.research.rutgers.edu/~qsliu/
（22）計算機視覺最新資訊網； http://www.cvchina.info/
（23）運動檢測、陰影、跟蹤的測試視頻下載；http://apps.hi.baidu.com/share/detail/18903287
（24）香港中文大學助理教授王曉剛； http://www.ee.cuhk.edu.hk/~xgwang/
(25)香港中文大學多媒體實驗室（湯曉鷗）; http://mmlab.ie.cuhk.edu.hk/
(26)U.C. San Diego. computer vision;http://vision.ucsd.edu/content/home
(27)CVonline; http://homepages.inf.ed.ac.uk/rbf/CVonline/
(28)computer vision software; http://peipa.essex.ac.uk/info/software.html
(29)Computer Vision Resource; http://www.cvpapers.com/
(30)computer vision research groups;http://peipa.essex.ac.uk/info/groups.html
(31)computer vision center; http://computervisioncentral.com/cvcnews
(32)浙江大學圖像技術研究與應用（ITRA）團隊：http://www.dvzju.com/
(33)自動識別網：http://www.autoid-china.com.cn/
(34)清華大學章毓晉教授：http://www.tsinghua.edu.cn/publish/ee/4157/2010/20101217173552339241557/20101217173552339241557_.html
(35)頂級民用機器人研究小組Porf.Gary領導的Willow Garage:http://www.willowgarage.com/
(36)上海交通大學圖像處理與模式識別研究所：http://www.pami.sjtu.edu.cn/
(37)上海交通大學計算機視覺實驗室劉允才教授：http://www.visionlab.sjtu.edu.cn/
(38)德克薩斯州大學奧斯汀分校助理教授Kristen Grauman ：http://www.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）：http://ocrserv.ee.tsinghua.edu.cn/auto/index.asp
(40)北京大學高文教授：http://www.jdl.ac.cn/htm-gaowen/
(41)清華大學艾海舟教授：http://media.cs.tsinghua.edu.cn/cn/aihz
(42)中科院生物識別與安全技術研究中心：http://www.cbsr.ia.ac.cn/china/index%20CH.asp
(43)瑞士巴塞爾大學 Thomas Vetter教授：http://informatik.unibas.ch/personen/vetter_t.html
(44)俄勒岡州立大學 Rob Hess博士：http://blogs.oregonstate.edu/hess/
(45)深圳大學 於仕祺副教授：http://yushiqi.cn/
(46)西安交通大學人工智能與機器人研究所：http://www.aiar.xjtu.edu.cn/
(47)卡內基梅隆大學研究員Robert T. Collins:http://www.cs.cmu.edu/~rcollins/home.html#Background
(48)MIT博士Chris Stauffer:http://people.csail.mit.edu/stauffer/Home/index.php
(49)美國密歇根州立大學生物識別研究組(Anil K. Jain教授)：http://www.cse.msu.edu/rgroups/biometrics/
(50)美國伊利諾伊州立大學Thomas S. Huang:http://www.beckman.illinois.edu/directory/t-huang1
(51)武漢大學數字攝影測量與計算機視覺研究中心：http://www.whudpcv.cn/index.asp
(52)瑞士巴塞爾大學Sami Romdhani助理研究員：http://informatik.unibas.ch/personen/romdhani_sami/
(53)CMU大學研究員Yang Wang:http://www.cs.cmu.edu/~wangy/home.html
(54)英國曼徹斯特大學Tim Cootes教授：http://personalpages.manchester.ac.uk/staff/timothy.f.cootes/
(55)美國羅徹斯特大學教授Jiebo Luo:http://www.cs.rochester.edu/u/jluo/
(56)美國普渡大學機器人視覺實驗室：https://engineering.purdue.edu/RVL/Welcome.html
(57)美國賓利州立大學感知、運動與認識實驗室：http://vision.cse.psu.edu/home/home.shtml
(58)美國賓夕法尼亞大學GRASP實驗室：https://www.grasp.upenn.edu/
(59)美國內達華大學裏諾校區CV實驗室：http://www.cse.unr.edu/CVL/index.php
(60)美國密西根大學vision實驗室：http://www.eecs.umich.edu/vision/index.html
(61)University of Massachusetts(麻省大學),視覺實驗室：http://vis-www.cs.umass.edu/index.html
(62)華盛頓大學博士後Iva Kemelmacher:http://www.cs.washington.edu/homes/kemelmi
(63)以色列魏茨曼科技大學Ronen Basri:http://www.wisdom.weizmann.ac.il/~ronen/index.html
(64)瑞士ETH-Zurich大學CV實驗室：http://www.vision.ee.ethz.ch/boostingTrackers/index.htm
(65)微軟CV研究員張正友：http://research.microsoft.com/en-us/um/people/zhang/
(66)中科院自動化所醫學影像研究室：http://www.3dmed.net/
(67)中科院田捷研究員：http://www.3dmed.net/tian/
(68)微軟Redmond研究院研究員Simon Baker:http://research.microsoft.com/en-us/people/sbaker/
(69)普林斯頓大學教授李凱：http://www.cs.princeton.edu/~li/
(70)普林斯頓大學博士賈登：http://www.cs.princeton.edu/~jiadeng/
(71)牛津大學教授Andrew Zisserman： http://www.robots.ox.ac.uk/~az/
(72)英國leeds大學研究員Mark Everingham:http://www.comp.leeds.ac.uk/me/
(73)英國愛丁堡大學教授Chris William: http://homepages.inf.ed.ac.uk/ckiw/
(74)微軟劍橋研究院研究員John Winn: http://johnwinn.org/
(75)佐治亞理工學院教授Monson H.Hayes：http://savannah.gatech.edu/people/mhayes/index.html
(76)微軟亞洲研究院研究員孫劍：http://research.microsoft.com/en-us/people/jiansun/
(77)微軟亞洲研究院研究員馬毅：http://research.microsoft.com/en-us/people/mayi/
(78)英國哥倫比亞大學教授David Lowe: http://www.cs.ubc.ca/~lowe/
(79)英國愛丁堡大學教授Bob Fisher: http://homepages.inf.ed.ac.uk/rbf/
(80)加州大學聖地亞哥分校教授Serge J.Belongie:http://cseweb.ucsd.edu/~sjb/
(81)威斯康星大學教授Charles R.Dyer: http://pages.cs.wisc.edu/~dyer/
(82)多倫多大學教授Allan.Jepson: http://www.cs.toronto.edu/~jepson/
(83)倫斯勒理工學院教授Qiang Ji: http://www.ecse.rpi.edu/~qji/
(84)CMU研究員Daniel Huber: http://www.ri.cmu.edu/person.html?person_id=123
(85)多倫多大學教授：David J.Fleet: http://www.cs.toronto.edu/~fleet/
(86)倫敦大學瑪麗女王學院教授Andrea Cavallaro:http://www.eecs.qmul.ac.uk/~andrea/
(87)多倫多大學教授Kyros Kutulakos: http://www.cs.toronto.edu/~kyros/
(88)杜克大學教授Carlo Tomasi: http://www.cs.duke.edu/~tomasi/
(89)CMU教授Martial Hebert: http://www.cs.cmu.edu/~hebert/
(90)MIT助理教授Antonio Torralba: http://web.mit.edu/torralba/www/
(91)馬里蘭大學研究員Yasel Yacoob: http://www.umiacs.umd.edu/users/yaser/
(92)康奈爾大學教授Ramin Zabih: http://www.cs.cornell.edu/~rdz/
(93)CMU博士田淵棟: http://www.cs.cmu.edu/~yuandong/
(94)CMU副教授Srinivasa Narasimhan: http://www.cs.cmu.edu/~srinivas/
(95)CMU大學ILIM實驗室：http://www.cs.cmu.edu/~ILIM/
(96)哥倫比亞大學教授Sheer K.Nayar: http://www.cs.columbia.edu/~nayar/
(97)三菱電子研究院研究員Fatih Porikli ：http://www.porikli.com/
(98)康奈爾大學教授Daniel Huttenlocher：http://www.cs.cornell.edu/~dph/
(99)南京大學教授周志華：http://cs.nju.edu.cn/zhouzh/index.htm
(100)芝加哥豐田技術研究所助理教授Devi Parikh: http://ttic.uchicago.edu/~dparikh/index.html
(101)瑞士聯邦理工學院博士後Helmut Grabner:http://www.vision.ee.ethz.ch/~hegrabne/#Short_CV
(102)香港中文大學教授賈佳亞：http://www.cse.cuhk.edu.hk/~leojia/index.html
(103)南京大學教授吳建鑫：http://c2inet.sce.ntu.edu.sg/Jianxin/index.html
(104)GE研究院研究員李關：http://www.cs.unc.edu/~lguan/
(105)佐治亞理工學院教授Monson Hayes:http://savannah.gatech.edu/people/mhayes/
(106)圖片檢索國際競賽PASCAL VOC(微軟劍橋研究院組織):http://pascallin.ecs.soton.ac.uk/challenges/VOC/
(107)機器視覺開源處理庫彙總：http://archive.cnblogs.com/a/2217609/
(108)布朗大學教授Benjamin Kimia: http://www.lems.brown.edu/kimia.html
(109)數據堂-圖像處理相關的樣本數據：http://www.datatang.com/data/list/602026/p1
(110)東軟基於CV的汽車輔助駕駛系統：http://www.neusoft.com/cn/solutions/1047/
(111)馬里蘭大學教授Rema Chellappa:http://www.cfar.umd.edu/~rama/
(112)芝加哥豐田研究中心助理教授Devi Parikh：http://ttic.uchicago.edu/~dparikh/index.html
(113)賓夕法尼亞大學助理教授石建波：http://www.cis.upenn.edu/~jshi/
(114)比利時魯汶大學教授Luc Van Gool：http://www.vision.ee.ethz.ch/members/get_member.cgi?id=1, http://www.vision.ee.ethz.ch/~vangool/
(115)行人檢測主頁：http://www.pedestrian-detection.com/
(116)法國學習算法與系統實驗室Basilio Noris博士：http://lasa.epfl.ch/people/member.php?SCIPER=129576 http://mldemos.epfl.ch/
(117)美國馬里蘭大學LARRY S.DAVIS教授：http://www.umiacs.umd.edu/~lsd/
(118)計算機視覺論文分類導航：http://www.visionbib.com/bibliography/contents.html
(119)計算機視覺分類信息導航：http://www.visionbib.com/
(120)西班牙馬德里理工大學博士Marcos Nieto：http://marcosnieto.net/
(121)香港理工大學副教授張磊：http://www4.comp.polyu.edu.hk/~cslzhang/
(122)以色列技術學院教授Michael Elad：http://www.cs.technion.ac.il/~elad/
(123)韓國啓明大學計算機視覺與模式識別實驗室：http://cvpr.kmu.ac.kr/
(124)英國諾丁漢大學Michel Valstar博士：http://www.cs.nott.ac.uk/~mfv/
(125)卡內基梅隆大學Takeo Kanade教授:http://www.ri.cmu.edu/people/kanade_takeo.html
(126)微軟學術搜索：http://libra.msra.cn/
(127)比利時天主教魯汶大學Radu Timofte博士：http://homes.esat.kuleuven.be/~rtimofte/，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:http://www.iainm.com/iainm/Home.html
http://www.ri.cmu.edu/person.html?type=publications&person_id=741 AAM,三維重建
（129）康奈爾大學視覺與圖像分析組：http://www.via.cornell.edu/ 醫學圖像處理
（130）密西根州立大學生物識別研究組：http://www.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：http://www.cv.tu-berlin.de/menue/computer_vision_remote_sensing/parameter/en/ 圖像分析、物體重建、基於圖像的表面測量、醫學圖像處理
（132）英國布里斯托大學數字多媒體研究組：http://www.cs.bris.ac.uk/Research/Digitalmedia/ 運動檢測與跟蹤、視頻壓縮、3D重建、字符定位
（133）英國薩利大學視覺、語音與信號處理中心： http://www.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：http://www.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：http://users.cecs.anu.edu.au/~hartley/ 運動估計、稀疏子空間、跟蹤、
（136）百度技術副總監於凱：http://www.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：http://web.xidian.edu.cn/xbgao/index.html 質量評判、水印、稀疏表示、超分辨率
（138）加州大學伯克利分校Michael I.Jordan教授：http://www.cs.berkeley.edu/~jordan/ 機器學習
（139）加州理工行人檢測相關資料：http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/
（140）微軟Redmond研究院研究員Piotr Dollar: http://vision.ucsd.edu/~pdollar/ 行人檢測、特徵提取、
（141）視覺計算研究論壇：http://www.sigvc.org/bbs/ 中科院視覺計算研究小組的論壇
（142）美國坦桑尼亞州立大學稀疏學習軟件包：http://www.public.asu.edu/~jye02/Software/SLEP/index.htm 稀疏學習
（143）美國加州大學聖地亞哥分校Jacob Whitehill博士：http://mplab.ucsd.edu/~jake/ 機器學習
（144）美國布朗大學Michael J.Black教授：http://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：http://cseweb.ucsd.edu/~kriegman/ 人臉識別
（146）南加州大學Paul Debevec教授：http://ict.debevec.org/~debevec/ 或 http://www.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：http://luthuli.cs.uiuc.edu/~daf/ 三維重建
（148）英國牛津大學Ian Reid教授：http://www.robots.ox.ac.uk/~ian/ 跟蹤和機器人導航
（149）CMU大學Alyosha Efros 教授: https://www.cs.cmu.edu/~efros/ 圖像紋理合成
（150）加州大學伯克利分校Jitendra Malik教授：http://www.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： http://people.csail.mit.edu/billf/ 圖像紋理合成
（152）CMU博士Henry Schneiderman： http://www.cs.cmu.edu/~hws/ 目標檢測和識別；
（153）微軟研究員Paul Viola: http://research.microsoft.com/en-us/um/people/viola/ AdaBoost算法
（154）微軟研究員Antonio Criminisi: http://research.microsoft.com/en-us/people/antcrim/ 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: http://www.wisdom.weizmann.ac.il/~irani/ 超分辨率
（156）瑞士洛桑理工學院Pascal Fua教授：http://people.epfl.ch/pascal.fua/bio?lang=en 立體視覺，增強現實
（157）佐治亞理工學院Irfan Essa教授：http://www.ic.gatech.edu/people/irfan-essa 人臉表情識別
（158）中科院助理教授樊彬：http://www.sigvc.org/bfan/ 特徵描述；
（159）斯坦福大學Sebastian Thrun教授：http://robots.stanford.edu/index.html 機器人；
（160）多倫多大學Geoffrey E.Hinton教授：http://www.cs.toronto.edu/~hinton/ 深度學習
（161）鳳巢系統架構師張棟博士：http://weibo.com/machinelearning
（162）2012年龍星計劃機器學習課程：http://bigeye.au.tsinghua.edu.cn/DragonStar2012/index.html
（163）中科院自動化所肖柏華教授：http://www.compsys.ia.ac.cn/people/xiaobaihua.html 文字識別、人臉識別、質量評判
（164）圖像視頻質量評判：http://live.ece.utexas.edu/research/quality/
（165）紐約大學Yann LeCun教授http://yann.lecun.com/ http://yann.lecun.com/exdb/mnist/ 手寫體數字識別
（166）二維條碼識別開源庫zxing：http://code.google.com/p/zxing/
（167）布朗大學Pedro Felzenszwalb教授：http://cs.brown.edu/~pff/ 特徵提取，Deformable Part Model
（168）伊利諾伊香檳大學Svetlana Lazebnik教授：http://www.cs.illinois.edu/homes/slazebni/ 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http://www.cs.uu.nl/centers/give/multimedia/index.html 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：http://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：http://www.cbsr.ia.ac.cn/users/znsun/ 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：http://www.jstuoke.com/web/xky/detail.asp?NewsID=1096 人臉圖像分析、醫學圖像分析
（173）清華大學助理教授馮建江：http://ivg.au.tsinghua.edu.cn/~jfeng/ 指紋識別
（174）北航助理教授黃迪：http://irip.buaa.edu.cn/~dihuang/ 3D人臉識別
（175）中山大學助理教授鄭偉詩：http://sist.sysu.edu.cn/~zhwshi/ 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: http://thomas.deselaers.de/index.html 圖像檢索
（177）百度深度學習研究中心博士後餘軼南：http://www.cbsr.ia.ac.cn/users/ynyu/index.htm 目標檢測，圖像檢索
（178）威茲曼科技大學超分辨率：http://www.wisdom.weizmann.ac.il/~vision/SingleImageSR.html
（179）德克薩斯大學奧斯汀分校Al Bovik教授：http://live.ece.utexas.edu/people/bovik/ 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：http://www.cs.huji.ac.il/~yweiss/ 機器學習、超分辨率
（181）以色列希伯來大學Daniel Zoran博士：http://www.cs.huji.ac.il/~daniez/ 超分辨率、去噪
（182）美國加州大學Peyman Milanfar教授：http://users.soe.ucsc.edu/~milanfar/ 去噪
（183）中科院計算所副研究員常虹：http://www.jdl.ac.cn/user/hchang/index.html 圖像檢索、半監督學習、超分辨率
（184）以色列威茨曼大學Anat Levin教授：http://www.wisdom.weizmann.ac.il/~levina/ 去噪、去模糊
（185）以色列威茨曼大學Daniel Glasner博士後：http://www.wisdom.weizmann.ac.il/~glasner/ 超分辨率、分割、姿態估計
（186）密西根大學助理教授Honglak Lee: http://web.eecs.umich.edu/~honglak/ 機器學習、特徵提取，去噪、稀疏表示；
（187）MIT周博磊博士：http://people.csail.mit.edu/bzhou/ 聚集分析、運動檢測
（188）美國田納西大學Li He博士：http://web.eecs.utk.edu/~lhe4/ 稀疏表示、超分辨率；
（189）Adobe研究院Jianchao Yang研究員：http://www.ifp.illinois.edu/~jyang29/ 稀疏表示，超分辨率、圖片檢索、去噪、去模糊
（190）Deep Learning主頁：http://deeplearning.net/ 深度學習論文、軟件，代碼，demo，數據等；
（191）斯坦福大學Andrew Ng教授：http://cs.stanford.edu/people/ang/ 深度神經網絡，深度學習
（192）Elefant: http://elefant.developer.nicta.com.au/ 機器學習開源庫
（193）微軟研究員Ce Liu: http://people.csail.mit.edu/celiu/ 去噪、超分辨率、去模糊、分割
（194）West Virginia大學助理教授Xin Li: http://www.csee.wvu.edu/~xinl/ 邊緣檢測、降噪、去模糊
（195）http://www.csee.wvu.edu/~xinl/source.html 深度學習、去噪、編碼、壓縮感知、超分辨率、聚類、分割等相關代碼集合
（196）西班牙格拉納達大學超分辨率重建項目組：http://decsai.ugr.es/pi/superresolution/index.html
（197）清華大學程明明博士：http://mmcheng.net/ 圖像分割、檢索
（198）牛津布魯克斯大學Philip H.S.Torr教授：http://cms.brookes.ac.uk/staff/PhilipTorr/ 分割、三維重建
（199）佐治亞理工學院James M.Rehg教授：http://www.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）:
http://www.image-net.org/challenges/LSVRC/2013/
（201）加州大學爾灣分校Deva Ramanan助理教授：http://www.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：http://www.mlcv.net/
（203）美國西北大學博士Ming Yang: http://www.ece.northwestern.edu/~mya671/ 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：http://www.cs.berkeley.edu/~rbg/ 目標檢測（DPM）
（205）中文語言資源聯盟：http://www.chineseldc.org/index.html 內有很多語言識別、字符識別的訓練，測試庫；
（206）西班牙巴塞羅那大學計算機視覺中心：http://www.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：http://www.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：http://www.vision.ee.ethz.ch/~aess/ 行人檢測、行爲檢測、跟蹤
（209）Libqrencode: http://fukuchi.org/works/qrencode/ 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：http://sit.jxufe.cn/grbk/yfn/index.html# 煙霧檢測、3D重建、醫學圖像處理
（211）耶路撒冷大學Raanan Fattal教師：http://www.cs.huji.ac.il/~raananf/ 圖像增強、
（212）耶路撒冷大學Dani Lischnski教授：http://www.cs.huji.ac.il/~danix/ 去模糊、紋理合成、圖像增強
3 代碼彙總
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]
-
Boundary Preserving Dense Local Regions [15][Project]
-
Weighted Histogram[Code]
-
An OpenCV - C++ implementation of Local Self Similarity Descriptors [Project]
-
Fast Sparse Representation with Prototypes[Project]
-
Corner Detection [Project]
-
AGAST Corner Detector: faster than FAST and even FAST-ER[Project]
-
Real-time Facial Feature Detection using Conditional Regression Forests[Project]
-
Global and Efficient Self-Similarity for Object Classification and Detection[code]
-
WαSH: Weighted α-Shapes for Local Feature Detection[Project]
-
HOG[Project]
-
Online Selection of Discriminative Tracking Features[Project]
二、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]
-
Efficient Planar Graph Cuts with Applications in Computer Vision[Paper][Code]
-
Isoperimetric Graph Partitioning for Image Segmentation[Paper][Code]
-
Blossom V: A new implementation of a minimum cost perfect matching algorithm[Code]
-
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision[Paper][Code]
-
Geodesic Star Convexity for Interactive Image Segmentation[Project]
-
Contour Detection and Image Segmentation Resources[Project][Code]
-
Biased Normalized Cuts[Project]
-
Max-flow/min-cut[Project]
-
Chan-Vese Segmentation using Level Set[Project]
-
A Toolbox of Level Set Methods[Project]
-
Re-initialization Free Level Set Evolution via Reaction Diffusion[Project]
-
A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction[Paper][Code]
-
Level Set Method Research by Chunming Li[Project]
-
ClassCut for Unsupervised Class Segmentation[code]
-
SEEDS: Superpixels Extracted via Energy-Driven Sampling [Project][other]
三、目標檢測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]
-
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、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]
-
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]
-
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相關：
-
Shape From Shading Using Linear Approximation[Code]
-
Combining Shape from Shading and Stereo Depth Maps[Project][Code]
-
A Spatio-Temporal Descriptor based on 3D Gradients (HOG3D)[Project][Code]
-
Multi-camera Scene Reconstruction via Graph Cuts[Paper][Code]
-
A Fast Marching Formulation of Perspective Shape from Shading under Frontal Illumination[Paper][Code]
-
Reconstruction:3D Shape, Illumination, Shading, Reflectance, Texture[Project]
-
Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers[Code]
-
Learning 3-D Scene Structure from a Single Still Image[Project]
十、機器學習算法：
-
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 (餘凱)[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、目標、行爲識別Object, Action Recognition：
-
Action Recognition Using a Distributed Representation of Pose and Appearance[Project]
-
2D Articulated Human Pose Estimation[Project]
-
Fast Human Pose Estimation Using Appearance and Motion via Multi-Dimensional Boosting Regression[Paper][Code]
-
Quasi-dense wide baseline matching[Project]
-
ChaLearn Gesture Challenge: Principal motion: PCA-based reconstruction of motion histograms[Project]
-
Real Time Head Pose Estimation with Random Regression Forests[Project]
-
2D Action Recognition Serves 3D Human Pose Estimation[
-
A Hough Transform-Based Voting Framework for Action Recognition[
-
Motion Interchange Patterns for Action Recognition in Unconstrained Videos[
-
2D articulated human pose estimation software[Project]
-
Learning and detecting shape models [code]
-
Progressive Search Space Reduction for Human Pose Estimation[Project]
-
Learning Non-Rigid 3D Shape from 2D Motion[Project]
十二、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：
十八、馬爾科夫隨機場Markov Random Fields：
-
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]
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A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications [Project]
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changedetection.net: A new change detection benchmark dataset[Project]
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ViBe - a powerful technique for background detection and subtraction in video sequences[Project]
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Background Subtraction Program[Project]
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Motion Detection Algorithms[Project]
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Stuttgart Artificial Background Subtraction Dataset[Project]
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Object Detection, Motion Estimation, and Tracking[Project]
Feature Detection and Description
General Libraries:
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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
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OpenCV – Various implementations of modern feature detectors and descriptors (SIFT, SURF, FAST, BRIEF, ORB, FREAK, etc.)
Fast Keypoint Detectors for Real-time Applications:
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FAST – High-speed corner detector implementation for a wide variety of platforms
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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:
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BRIEF – C++ code for a fast and accurate interest point descriptor (not invariant to rotations and scale) (ECCV 2010)
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ORB – OpenCV implementation of the Oriented-Brief (ORB) descriptor (invariant to rotations, but not scale)
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BRISK – Efficient Binary descriptor invariant to rotations and scale. It includes a Matlab mex interface. (ICCV 2011)
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FREAK – Faster than BRISK (invariant to rotations and scale) (CVPR 2012)
SIFT and SURF Implementations:
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SIFT: VLFeat, OpenCV, Original code by David Lowe, GPU implementation, OpenSIFT
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SURF: Herbert Bay’s code, OpenCV, GPU-SURF
Other Local Feature Detectors and Descriptors:
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VGG Affine Covariant features – Oxford code for various affine covariant feature detectors and descriptors.
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LIOP descriptor – Source code for the Local Intensity order Pattern (LIOP) descriptor (ICCV 2011).
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Local Symmetry Features – Source code for matching of local symmetry features under large variations in lighting, age, and rendering style (CVPR 2012).
Global Image Descriptors:
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GIST – Matlab code for the GIST descriptor
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CENTRIST – Global visual descriptor for scene categorization and object detection (PAMI 2011)
Feature Coding and Pooling
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VGG Feature Encoding Toolkit – Source code for various state-of-the-art feature encoding methods – including Standard hard encoding, Kernel codebook encoding, Locality-constrained linear encoding, and Fisher kernel encoding.
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Spatial Pyramid Matching – Source code for feature pooling based on spatial pyramid matching (widely used for image classification)
Convolutional Nets and Deep Learning
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EBLearn – C++ Library for Energy-Based Learning. It includes several demos and step-by-step instructions to train classifiers based on convolutional neural networks.
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Torch7 – Provides a matlab-like environment for state-of-the-art machine learning algorithms, including a fast implementation of convolutional neural networks.
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Deep Learning - Various links for deep learning software.
Part-Based Models
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Deformable Part-based Detector – Library provided by the authors of the original paper (state-of-the-art in PASCAL VOC detection task)
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Efficient Deformable Part-Based Detector – Branch-and-Bound implementation for a deformable part-based detector.
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Accelerated Deformable Part Model – Efficient implementation of a method that achieves the exact same performance of deformable part-based detectors but with significant acceleration (ECCV 2012).
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Coarse-to-Fine Deformable Part Model – Fast approach for deformable object detection (CVPR 2011).
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Poselets – C++ and Matlab versions for object detection based on poselets.
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Part-based Face Detector and Pose Estimation – Implementation of a unified approach for face detection, pose estimation, and landmark localization (CVPR 2012).
Attributes and Semantic Features
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Relative Attributes – Modified implementation of RankSVM to train Relative Attributes (ICCV 2011).
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Object Bank – Implementation of object bank semantic features (NIPS 2010). See also ActionBank
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Classemes, Picodes, and Meta-class features – Software for extracting high-level image descriptors (ECCV 2010, NIPS 2011, CVPR 2012).
Large-Scale Learning
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Additive Kernels – Source code for fast additive kernel SVM classifiers (PAMI 2013).
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LIBLINEAR – Library for large-scale linear SVM classification.
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VLFeat – Implementation for Pegasos SVM and Homogeneous Kernel map.
Fast Indexing and Image Retrieval
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FLANN – Library for performing fast approximate nearest neighbor.
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Kernelized LSH – Source code for Kernelized Locality-Sensitive Hashing (ICCV 2009).
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ITQ Binary codes – Code for generation of small binary codes using Iterative Quantization and other baselines such as Locality-Sensitive-Hashing (CVPR 2011).
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INRIA Image Retrieval – Efficient code for state-of-the-art large-scale image retrieval (CVPR 2011).
Object Detection
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See Part-based Models and Convolutional Nets above.
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Pedestrian Detection at 100fps – Very fast and accurate pedestrian detector (CVPR 2012).
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Caltech Pedestrian Detection Benchmark – Excellent resource for pedestrian detection, with various links for state-of-the-art implementations.
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OpenCV – Enhanced implementation of Viola&Jones real-time object detector, with trained models for face detection.
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Efficient Subwindow Search – Source code for branch-and-bound optimization for efficient object localization (CVPR 2008).
3D Recognition
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Point-Cloud Library – Library for 3D image and point cloud processing.
Action Recognition
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ActionBank – Source code for action recognition based on the ActionBank representation (CVPR 2012).
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STIP Features – software for computing space-time interest point descriptors
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Independent Subspace Analysis – Look for Stacked ISA for Videos (CVPR 2011)
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Velocity Histories of Tracked Keypoints - C++ code for activity recognition using the velocity histories of tracked keypoints (ICCV 2009)
Datasets
Attributes
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Animals with Attributes – 30,475 images of 50 animals classes with 6 pre-extracted feature representations for each image.
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aYahoo and aPascal – Attribute annotations for images collected from Yahoo and Pascal VOC 2008.
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FaceTracer – 15,000 faces annotated with 10 attributes and fiducial points.
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PubFig – 58,797 face images of 200 people with 73 attribute classifier outputs.
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[url=http://vis-www.cs.umass.edu/lfw/]LFW[/url] – 13,233 face images of 5,749 people with 73 attribute classifier outputs.
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Human Attributes – 8,000 people with annotated attributes. Check also this link for another dataset of human attributes.
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SUN Attribute Database – Large-scale scene attribute database with a taxonomy of 102 attributes.
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ImageNet Attributes – Variety of attribute labels for the ImageNet dataset.
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Relative attributes – Data for OSR and a subset of PubFig datasets. Check also this link for the WhittleSearch data.
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Attribute Discovery Dataset – Images of shopping categories associated with textual descriptions.
Fine-grained Visual Categorization
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Caltech-UCSD Birds Dataset – Hundreds of bird categories with annotated parts and attributes.
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Stanford Dogs Dataset – 20,000 images of 120 breeds of dogs from around the world.
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Oxford-IIIT Pet Dataset – 37 category pet dataset with roughly 200 images for each class. Pixel level trimap segmentation is included.
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Leeds Butterfly Dataset – 832 images of 10 species of butterflies.
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Oxford Flower Dataset – Hundreds of flower categories.
Face Detection
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[url=http://vis-www.cs.umass.edu/fddb/]FDDB[/url] – UMass face detection dataset and benchmark (5,000+ faces)
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CMU/MIT – Classical face detection dataset.
Face Recognition
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Face Recognition Homepage – Large collection of face recognition datasets.
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[url=http://vis-www.cs.umass.edu/lfw/]LFW[/url] – UMass unconstrained face recognition dataset (13,000+ face images).
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NIST Face Homepage – includes face recognition grand challenge (FRGC), vendor tests (FRVT) and others.
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CMU Multi-PIE – contains more than 750,000 images of 337 people, with 15 different views and 19 lighting conditions.
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FERET – Classical face recognition dataset.
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Deng Cai’s face dataset in Matlab Format – Easy to use if you want play with simple face datasets including Yale, ORL, PIE, and Extended Yale B.
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SCFace – Low-resolution face dataset captured from surveillance cameras.
Handwritten Digits
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MNIST – large dataset containing a training set of 60,000 examples, and a test set of 10,000 examples.
Pedestrian Detection
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Caltech Pedestrian Detection Benchmark – 10 hours of video taken from a vehicle,350K bounding boxes for about 2.3K unique pedestrians.
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INRIA Person Dataset – Currently one of the most popular pedestrian detection datasets.
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ETH Pedestrian Dataset – Urban dataset captured from a stereo rig mounted on a stroller.
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TUD-Brussels Pedestrian Dataset – Dataset with image pairs recorded in an crowded urban setting with an onboard camera.
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PASCAL Human Detection – One of 20 categories in PASCAL VOC detection challenges.
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USC Pedestrian Dataset – Small dataset captured from surveillance cameras.
Generic Object Recognition
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ImageNet – Currently the largest visual recognition dataset in terms of number of categories and images.
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Tiny Images – 80 million 32x32 low resolution images.
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Pascal VOC – One of the most influential visual recognition datasets.
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Caltech 101 / Caltech 256 – Popular image datasets containing 101 and 256 object categories, respectively.
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MIT LabelMe – Online annotation tool for building computer vision databases.
Scene Recognition
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MIT SUN Dataset – MIT scene understanding dataset.
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UIUC Fifteen Scene Categories – Dataset of 15 natural scene categories.
Feature Detection and Description
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VGG Affine Dataset – Widely used dataset for measuring performance of feature detection and description. CheckVLBenchmarksfor an evaluation framework.
Action Recognition
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Benchmarking Activity Recognition – CVPR 2012 tutorial covering various datasets for action recognition.
RGBD Recognition
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RGB-D Object Dataset – Dataset containing 300 common household objects
Reference:
[1]: http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html
特徵提取-
SURF特徵: http://www.vision.ee.ethz.ch/software/index.de.html(當然這只是其中之一)
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LBP特徵(一種紋理特徵)：http://www.comp.hkbu.edu.hk/~icpr06/tutorials/Pietikainen.html
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Fast Corner Detection（OpenCV中的Fast算法）:FAST Corner Detection -- Edward Rosten
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A simple object detector with boosting(Awarded the Best Short Course Prize at ICCV 2005，So瞭解adaboost的推薦之作)：http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html
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Boosting(該網頁上有相當全的Boosting的文章和幾個Boosting代碼，本人推薦)：http://cbio.mskcc.org/~aarvey/boosting_papers.html
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Adaboost Matlab 工具：http://graphics.cs.msu.ru/en/science/research/machinelearning/adaboosttoolbox
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MultiBoost(不說啥了，多類Adaboost算法的程序)：http://sourceforge.net/projects/multiboost/
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TextonBoost(我們教研室王冠夫師兄的畢設): Jamie Shotton - Code
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Conditional Random Fields（CRF論文+Code列表，推薦）
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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）
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CvPapers(好吧，牛吧網站，裏面有ICCV，CVPR，ECCV，SIGGRAPH的論文收錄，然後還有一些論文的代碼蒐集，要求加精！)：http://www.cvpapers.com/
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Computer Vision Software(裏面代碼很多，並詳細的給出了分類)：http://peipa.essex.ac.uk/info/software.html
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某人的Windows Live（我看裏面東東不少就收藏了）：https://skydrive.live.com/?cid=3b6244088fd5a769#cid=3B6244088FD5A769&id=3B6244088FD5A769!523
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MATLAB and Octave Functions for Computer Vision and Image Processing（這個裏面的東西也很全，只是都是用Matlab和Octave開發的）：http://www.csse.uwa.edu.au/~pk/research/matlabfns/
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Computer Vision Resources（裏面的視覺算法很多，給出了相應的論文和Code，挺好的）：https://netfiles.uiuc.edu/jbhuang1/www/resources/vision/index.html
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MATLAB Functions for Multiple View Geometry（關於物體多視角計算的庫）：http://www.robots.ox.ac.uk/~vgg/hzbook/code/
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Evolutive Algorithm based on Naïve Bayes models Estimation（單獨列了一個算法的Code）：http://www.cvc.uab.cat/~xbaro/eanbe/#_Software
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Jianxin Wu's homepage（就是上面的）
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Berkeley大學做的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：http://www.cs.berkeley.edu/~smaji/projects/ped-detector/
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High Speed Obstacle Avoidance using Monocular Vision and Reinforcement Learning
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TLD(2010年很火的tracking算法)
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Optical Flow Algorithm Evaluation (提供了一個動態貝葉斯網絡框架，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/
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卡爾曼濾波：The Kalman Filter(終極網頁)
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Bayesian Filtering Library: The Bayesian Filtering Library
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MATLAB Normalized Cuts Segmentation Code：software
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超像素分割：SLIC Superpixels
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參考：
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
