这两周，一直奔波于各种杂物，能留给看书，写东西的时间却越发少了总觉得很可惜好在今天在Git Hub 上翻到了一些好东西计算机视觉领域，你需要了解的都在这里了或许以后要给自己定个规矩，每周都要留下两三天就看资料，写东西才好腹无点墨，以后是怕要是更找不到妹子了话说根据新的广告法，若是大家转发给我介绍对象，是不是也得标注广告了？，我来为大家讲解一下关于机器视觉技术基础知识?跟着小编一起来看一看吧!
机器视觉技术基础知识
这两周，一直奔波于各种杂物，能留给看书，写东西的时间却越发少了。总觉得很可惜。好在今天在Git Hub 上翻到了一些好东西。计算机视觉领域，你需要了解的都在这里了。或许以后要给自己定个规矩，每周都要留下两三天就看资料，写东西才好。腹无点墨，以后是怕要是更找不到妹子了。话说根据新的广告法，若是大家转发给我介绍对象，是不是也得标注广告了？
OH No！！！！！无论新广告法怎样，但今天有个大安利，我是安利定了。今天在Git Hub 上翻到了一个项目，整理了计算机视觉领域的相关资料。计算机视觉领域，你需要了解的几乎都在这里了。如过你也想对这个项目做出贡献，你可以 email 项目的发起者 Jia-Bin Huang（jbhuang1@illinois.edu） 由于微信不能外链，很多贴出来的书目都有数字版的文档，大家可以在后台回复 十全大补丸 获取原文链接。
计算机视觉十全大补丸
发起人Jia-Bin Huang
研究方向： physically grounded visual synthesis and analysis.
书
计算机视觉：
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
机器学习：
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
基础：Linear Algebra and Its Applications - Gilbert Strang 1995
课程
计算机视觉：
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 UniversityComputational 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)
机器学习：
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)
优化：
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)
未完待续
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