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【原文】https://hackerlists.com/computer-vision-resources/
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【編輯】Major術業
TABLE OF CONTENTS
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
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
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 Universit?t 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)
（以上為300篇中的部分，完整內容請查看【Major術業】（ID：Major-2016）公眾號）
