这是一份详尽的计算机视觉资源清单，涵盖了书籍、在线课程、研究论文、软件工具、数据集和教程。无论你是初学者还是资深研究人员，都能在这里找到丰富的学习和参考资料，涉及领域包括图像处理、机器学习、深度学习、3D计算机视觉等。
Awesome Computer Vision
A curated list of awesome computer vision resources
Table of Contents
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
- Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics - Justin Solomon 2015
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 Spring 2016, Fall 2016 - 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)
- Computer Vision Pascal Fua (EPFL):
- Computer Vision 1 Carsten Rother (TU Dresden):
- Computer Vision 2 Carsten Rother (TU Dresden):
- Multiple View Geometry Daniel Cremers (TU Munich):
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)
- Intro to Machine Learning - Sebastian Thrun (Stanford University)
- Machine Learning - Charles Isbell, Michael Littman (Georgia Tech)
- (Convolutional) Neural Networks for Visual Recognition - Fei-Fei Li, Andrej Karphaty, Justin Johnson (Stanford University)
- Machine Learning for Computer Vision - Rudolph Triebel (TU Munich)
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 vis
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