A curated collection of 300+ awesome computer vision resources including books, courses, papers, tutorials, software and more.
Due to the size of this list, it can be hard to keep up with broken links, so if you come across any, please let me know in the comments section. Also, if you know of any more awesome computer vision resources than what is on this list, please let me know in the comments section.

TABLE OF CONTENTS

• Books
• Courses
• Papers
• Tutorials and Talks
• Software
• Datasets
• Resources for students
• Links

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

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)

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

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