【超级重磅】收藏!300多项优质资源,“计算机视觉”学习的终极列表

【来源】hackerlists

【原文】https://hackerlists.com/computer-vision-resources/

【备注】源链接为总链接,“阅读原文”可查看全部子链接

【编辑】Major术业

TABLE OF CONTENTS

Books

Courses

Papers

Tutorials and Talks

Software

Datasets

Resources for students

Links

1

BOOKS

COMPUTER VISION

Computer Vision: Models, Learning, and Inference– Simon J. D. Prince 2012

Computer Vision: Theory and Application– Rick Szeliski 2010

Computer Vision: A Modern Approach (2nd edition)– David Forsyth and Jean Ponce 2011

Multiple View Geometry in Computer Vision– Richard Hartley and Andrew Zisserman 2004

Computer Vision– Linda G. Shapiro 2001

Vision Science: Photons to Phenomenology– Stephen E. Palmer 1999

Visual Object Recognition synthesis lecture– Kristen Grauman and Bastian Leibe 2011

Computer Vision for Visual Effects– Richard J. Radke, 2012

High dynamic range imaging: acquisition, display, and image-based lighting– Reinhard, E., Heidrich, W., Debevec, P., Pattanaik, S., Ward, G., Myszkowski, K 2010

OPENCV PROGRAMMING

Learning OpenCV: Computer Vision with the OpenCV Library– Gary Bradski and Adrian Kaehler

Practical Python and OpenCV– Adrian Rosebrock

OpenCV Essentials– Oscar Deniz Suarez, Mª del Milagro Fernandez Carrobles, Noelia Vallez Enano, Gloria Bueno Garcia, Ismael Serrano Gracia

MACHINE LEARNING

Pattern Recognition and Machine Learning– Christopher M. Bishop 2007

Neural Networks for Pattern Recognition– Christopher M. Bishop 1995

Probabilistic Graphical Models: Principles and Techniques– Daphne Koller and Nir Friedman 2009

Pattern Classification– Peter E. Hart, David G. Stork, and Richard O. Duda 2000

Machine Learning– Tom M. Mitchell 1997

Gaussian processes for machine learning– Carl Edward Rasmussen and Christopher K. I. Williams 2005

Learning From Data– Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin 2012

Neural Networks and Deep Learning– Michael Nielsen 2014

Bayesian Reasoning and Machine Learning– David Barber, Cambridge University Press, 2012

FUNDAMENTALS

Linear Algebra and Its Applications– Gilbert Strang 1995

2

COURSES

COMPUTER VISION

EENG 512 / CSCI 512 – Computer Vision– William Hoff (Colorado School of Mines)

Visual Object and Activity Recognition– Alexei A. Efros and Trevor Darrell (UC Berkeley)

Computer Vision– Steve Seitz (University of Washington)

Visual Recognition– Kristen Grauman (UT Austin)

Language and Vision– Tamara Berg (UNC Chapel Hill)

Convolutional Neural Networks for Visual Recognition– Fei-Fei Li and Andrej Karpathy (Stanford University)

Computer Vision– Rob Fergus (NYU)

Computer Vision– Derek Hoiem (UIUC)

Computer Vision: Foundations and Applications– Kalanit Grill-Spector and Fei-Fei Li (Stanford University)

High-Level Vision: Behaviors, Neurons and Computational Models– Fei-Fei Li (Stanford University)

Advances in Computer Vision– Antonio Torralba and Bill Freeman (MIT)

Computer Vision– Bastian Leibe (RWTH Aachen University)

Computer Vision 2– Bastian Leibe (RWTH Aachen University)

COMPUTATIONAL PHOTOGRAPHY

Image Manipulation and Computational Photography– Alexei A. Efros (UC Berkeley)

Computational Photography– Alexei A. Efros (CMU)

Computational Photography– Derek Hoiem (UIUC)

Computational Photography– James Hays (Brown University)

Digital & Computational Photography– Fredo Durand (MIT)

Computational Camera and Photography– Ramesh Raskar (MIT Media Lab)

Computational Photography– Irfan Essa (Georgia Tech)

Courses in Graphics– Stanford University

Computational Photography– Rob Fergus (NYU)

Introduction to Visual Computing– Kyros Kutulakos (University of Toronto)

Computational Photography– Kyros Kutulakos (University of Toronto)

Computer Vision for Visual Effects– Rich Radke (Rensselaer Polytechnic Institute)

Introduction to Image Processing– Rich Radke (Rensselaer Polytechnic Institute)

MACHINE LEARNING AND STATISTICAL LEARNING

Machine Learning– Andrew Ng (Stanford University)

Learning from Data– Yaser S. Abu-Mostafa (Caltech)

Statistical Learning– Trevor Hastie and Rob Tibshirani (Stanford University)

Statistical Learning Theory and Applications– Tomaso Poggio, Lorenzo Rosasco, Carlo Ciliberto, Charlie Frogner, Georgios Evangelopoulos, Ben Deen (MIT)

Statistical Learning– Genevera Allen (Rice University)

Practical Machine Learning– Michael Jordan (UC Berkeley)

Course on Information Theory, Pattern Recognition, and Neural Networks– David MacKay (University of Cambridge)

Methods for Applied Statistics: Unsupervised Learning– Lester Mackey (Stanford)

Machine Learning– Andrew Zisserman (University of Oxford)

OPTIMIZATION

Convex Optimization I– Stephen Boyd (Stanford University)

Convex Optimization II– Stephen Boyd (Stanford University)

Convex Optimization– Stephen Boyd (Stanford University)

Optimization at MIT– (MIT)

Convex Optimization– Ryan Tibshirani (CMU)

3

PAPERS

CONFERENCE PAPERS ON THE WEB

CVPapers– Computer vision papers on the web

SIGGRAPH Paper on the web– Graphics papers on the web

NIPS Proceedings– NIPS papers on the web

Computer Vision Foundation open access

Annotated Computer Vision Bibliography– Keith Price (USC)

Calendar of Computer Image Analysis, Computer Vision Conferences– (USC)

SURVEY PAPERS

Visionbib Survey Paper List

Foundations and Trends® in Computer Graphics and Vision

Computer Vision: A Reference Guide

4

TUTORIALS AND TALKS

COMPUTER VISION

Computer Vision Talks– Lectures, keynotes, panel discussions on computer vision

The Three R’s of Computer Vision– Jitendra Malik (UC Berkeley) 2013

Applications to Machine Vision– Andrew Blake (Microsoft Research) 2008

The Future of Image Search– Jitendra Malik (UC Berkeley) 2008

Should I do a PhD in Computer Vision?– Fatih Porikli (Australian National University)

Graduate Summer School 2013: Computer Vision– IPAM, 2013

CONFERENCE TALKS

CVPR 2015– Jun 2015

ECCV 2014– Sep 2014

CVPR 2014– Jun 2014

ICCV 2013– Dec 2013

ICML 2013– Jul 2013

CVPR 2013– Jun 2013

ECCV 2012– Oct 2012

ICML 2012– Jun 2012

CVPR 2012– Jun 2012

3D COMPUTER VISION

3D Computer Vision: Past, Present, and Future– Steve Seitz (University of Washington) 2011

Reconstructing the World from Photos on the Internet– Steve Seitz (University of Washington) 2013

INTERNET VISION

The Distributed Camera– Noah Snavely (Cornell University) 2011

Planet-Scale Visual Understanding– Noah Snavely (Cornell University) 2014

A Trillion Photos– Steve Seitz (University of Washington) 2013

COMPUTATIONAL PHOTOGRAPHY

Reflections on Image-Based Modeling and Rendering– Richard Szeliski (Microsoft Research) 2013

Photographing Events over Time– William T. Freeman (MIT) 2011

Old and New algorithm for Blind Deconvolution– Yair Weiss (The Hebrew University of Jerusalem) 2011

A Tour of Modern “Image Processing”– Peyman Milanfar (UC Santa Cruz/Google) 2010

Topics in image and video processingAndrew Blake (Microsoft Research) 2007

Computational Photography– William T. Freeman (MIT) 2012

Revealing the Invisible– Frédo Durand (MIT) 2012

Overview of Computer Vision and Visual Effects– Rich Radke (Rensselaer Polytechnic Institute) 2014

LEARNING AND VISION

Where machine vision needs help from machine learning– William T. Freeman (MIT) 2011

Learning in Computer Vision– Simon Lucey (CMU) 2008

Learning and Inference in Low-Level Vision– Yair Weiss (The Hebrew University of Jerusalem) 2009

OBJECT RECOGNITION

Object Recognition– Larry Zitnick (Microsoft Research)

Generative Models for Visual Objects and Object Recognition via Bayesian Inference– Fei-Fei Li (Stanford University)

GRAPHICAL MODELS

Graphical Models for Computer Vision– Pedro Felzenszwalb (Brown University) 2012

Graphical Models– Zoubin Ghahramani (University of Cambridge) 2009

Machine Learning, Probability and Graphical Models– Sam Roweis (NYU) 2006

Graphical Models and Applications– Yair Weiss (The Hebrew University of Jerusalem) 2009

MACHINE LEARNING

A Gentle Tutorial of the EM Algorithm– Jeff A. Bilmes (UC Berkeley) 1998

Introduction To Bayesian Inference– Christopher Bishop (Microsoft Research) 2009

Support Vector Machines– Chih-Jen Lin (National Taiwan University) 2006

Bayesian or Frequentist, Which Are You?– Michael I. Jordan (UC Berkeley)

OPTIMIZATION

Optimization Algorithms in Machine Learning– Stephen J. Wright (University of Wisconsin-Madison)

Convex Optimization– Lieven Vandenberghe (University of California, Los Angeles)

Continuous Optimization in Computer Vision– Andrew Fitzgibbon (Microsoft Research)

Beyond stochastic gradient descent for large-scale machine learning– Francis Bach (INRIA)

Variational Methods for Computer Vision– Daniel Cremers (Technische Universität München) (lecture 18 missing from playlist)

DEEP LEARNING

A tutorial on Deep Learning– Geoffrey E. Hinton (University of Toronto)

Deep Learning– Ruslan Salakhutdinov (University of Toronto)

Scaling up Deep Learning– Yoshua Bengio (University of Montreal)

ImageNet Classification with Deep Convolutional Neural Networks– Alex Krizhevsky (University of Toronto)

The Unreasonable Effectivness Of Deep LearningYann LeCun (NYU/Facebook Research) 2014

Deep Learning for Computer Vision– Rob Fergus (NYU/Facebook Research)

High-dimensional learning with deep network contractions– Stéphane Mallat (Ecole Normale Superieure)

Graduate Summer School 2012: Deep Learning, Feature Learning– IPAM, 2012

Workshop on Big Data and Statistical Machine Learning

Machine Learning Summer School– Reykjavik, Iceland 2014

Deep Learning Session 1– Yoshua Bengio (Universtiy of Montreal)

Deep Learning Session 2– Yoshua Bengio (University of Montreal)

Deep Learning Session 3– Yoshua Bengio (University of Montreal)

(以上为300篇中的部分,完整内容请查看【Major术业】(ID:Major-2016)公众号)

最后编辑于
©著作权归作者所有,转载或内容合作请联系作者
  • 序言:七十年代末,一起剥皮案震惊了整个滨河市,随后出现的几起案子,更是在滨河造成了极大的恐慌,老刑警刘岩,带你破解...
    沈念sama阅读 175,490评论 5 419
  • 序言:滨河连续发生了三起死亡事件,死亡现场离奇诡异,居然都是意外死亡,警方通过查阅死者的电脑和手机,发现死者居然都...
    沈念sama阅读 74,060评论 2 335
  • 文/潘晓璐 我一进店门,熙熙楼的掌柜王于贵愁眉苦脸地迎上来,“玉大人,你说我怎么就摊上这事。” “怎么了?”我有些...
    开封第一讲书人阅读 124,407评论 0 291
  • 文/不坏的土叔 我叫张陵,是天一观的道长。 经常有香客问我,道长,这世上最难降的妖魔是什么? 我笑而不...
    开封第一讲书人阅读 47,741评论 0 248
  • 正文 为了忘掉前任,我火速办了婚礼,结果婚礼上,老公的妹妹穿的比我还像新娘。我一直安慰自己,他们只是感情好,可当我...
    茶点故事阅读 56,543评论 3 329
  • 文/花漫 我一把揭开白布。 她就那样静静地躺着,像睡着了一般。 火红的嫁衣衬着肌肤如雪。 梳的纹丝不乱的头发上,一...
    开封第一讲书人阅读 43,040评论 1 246
  • 那天,我揣着相机与录音,去河边找鬼。 笑死,一个胖子当着我的面吹牛,可吹牛的内容都是我干的。 我是一名探鬼主播,决...
    沈念sama阅读 34,107评论 3 358
  • 文/苍兰香墨 我猛地睁开眼,长吁一口气:“原来是场噩梦啊……” “哼!你这毒妇竟也来了?” 一声冷哼从身侧响起,我...
    开封第一讲书人阅读 32,646评论 0 229
  • 序言:老挝万荣一对情侣失踪,失踪者是张志新(化名)和其女友刘颖,没想到半个月后,有当地人在树林里发现了一具尸体,经...
    沈念sama阅读 36,694评论 1 271
  • 正文 独居荒郊野岭守林人离奇死亡,尸身上长有42处带血的脓包…… 初始之章·张勋 以下内容为张勋视角 年9月15日...
    茶点故事阅读 32,398评论 2 279
  • 正文 我和宋清朗相恋三年,在试婚纱的时候发现自己被绿了。 大学时的朋友给我发了我未婚夫和他白月光在一起吃饭的照片。...
    茶点故事阅读 33,987评论 1 288
  • 序言:一个原本活蹦乱跳的男人离奇死亡,死状恐怖,灵堂内的尸体忽然破棺而出,到底是诈尸还是另有隐情,我是刑警宁泽,带...
    沈念sama阅读 30,097评论 3 285
  • 正文 年R本政府宣布,位于F岛的核电站,受9级特大地震影响,放射性物质发生泄漏。R本人自食恶果不足惜,却给世界环境...
    茶点故事阅读 35,298评论 3 282
  • 文/蒙蒙 一、第九天 我趴在偏房一处隐蔽的房顶上张望。 院中可真热闹,春花似锦、人声如沸。这庄子的主人今日做“春日...
    开封第一讲书人阅读 27,278评论 0 14
  • 文/苍兰香墨 我抬头看了看天上的太阳。三九已至,却和暖如春,着一层夹袄步出监牢的瞬间,已是汗流浃背。 一阵脚步声响...
    开封第一讲书人阅读 28,413评论 1 232
  • 我被黑心中介骗来泰国打工, 没想到刚下飞机就差点儿被人妖公主榨干…… 1. 我叫王不留,地道东北人。 一个月前我还...
    沈念sama阅读 38,397评论 2 309
  • 正文 我出身青楼,却偏偏与公主长得像,于是被迫代替她去往敌国和亲。 传闻我的和亲对象是个残疾皇子,可洞房花烛夜当晚...
    茶点故事阅读 38,099评论 2 314

推荐阅读更多精彩内容