awesome-computer-vision

A curated list of awesome computer vision resources
https://github.com/eric-erki/awesome-computer-vision

Last synced: 8 days ago
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Blogs
- Andrej Karpathy blog - Andrej Karpathy
- AI Shack - Utkarsh Sinha
- Computer Vision Basics with Python Keras and OpenCV - Jason Chin (University of Western Ontario)
- Computer vision for dummies - Vincent Spruyt
- Computer Vision Talks - Eugene Khvedchenya
Books
- Computer Vision: Models, Learning, and Inference - Simon J. D. Prince 2012
- Computer Vision: Theory and Application - Rick Szeliski 2010
- Visual Object Recognition synthesis lecture - Kristen Grauman and Bastian Leibe 2011
- Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics - Justin Solomon 2015
- 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
- Neural Networks and Deep Learning - Michael Nielsen 2014
- Computer Vision: Models, Learning, and Inference - Simon J. D. Prince 2012
- Visual Object Recognition synthesis lecture - Kristen Grauman and Bastian Leibe 2011
- Pattern Classification - Peter E. Hart, David G. Stork, and Richard O. Duda 2000
- Neural Networks and Deep Learning - Michael Nielsen 2014
- Bayesian Reasoning and Machine Learning - David Barber, Cambridge University Press, 2012
- Linear Algebra and Its Applications - Gilbert Strang 1995
- Pattern Recognition and Machine Learning - Christopher M. Bishop 2007
- Computer Vision: A Modern Approach (2nd edition) - David Forsyth and Jean Ponce 2011
- Computer Vision - Linda G. Shapiro 2001
- OpenCV Essentials - Oscar Deniz Suarez, Mª del Milagro Fernandez Carrobles, Noelia Vallez Enano, Gloria Bueno Garcia, Ismael Serrano Gracia
- Vision Science: Photons to Phenomenology - Stephen E. Palmer 1999
- Learning OpenCV: Computer Vision with the OpenCV Library - Gary Bradski and Adrian Kaehler
- Machine Learning - Tom M. Mitchell 1997
- Multiple View Geometry in Computer Vision - Richard Hartley and Andrew Zisserman 2004
- High dynamic range imaging: acquisition, display, and image-based lighting - Reinhard, E., Heidrich, W., Debevec, P., Pattanaik, S., Ward, G., Myszkowski, K 2010
Contributing
- pull requests
Courses
- Learning from Data - Yaser S. Abu-Mostafa (Caltech)
- EENG 512 / CSCI 512 - Computer Vision - William Hoff (Colorado School of Mines)
- Convolutional Neural Networks for Visual Recognition - Fei-Fei Li and Andrej Karpathy (Stanford University)
- 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
- Computer Vision 1
- Computer Vision 2
- Computational Photography - Alexei A. Efros (CMU)
- Courses in Graphics - Stanford University
- Introduction to Visual Computing - Kyros Kutulakos (University of Toronto)
- Computational Photography - Kyros Kutulakos (University of Toronto)
- Machine Learning - Andrew Ng (Stanford University)
- 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)
- Methods for Applied Statistics: Unsupervised Learning - Lester Mackey (Stanford)
- Intro to Machine Learning - Sebastian Thrun (Stanford University)
- (Convolutional) Neural Networks for Visual Recognition - Fei-Fei Li, Andrej Karphaty, Justin Johnson (Stanford University)
- Machine Learning for Computer Vision - Rudolph Triebel (TU Munich)
- Convex Optimization II - Stephen Boyd (Stanford University)
- Convex Optimization - Stephen Boyd (Stanford University)
- Computational Photography - Derek Hoiem (UIUC)
- Image Manipulation and Computational Photography - Alexei A. Efros (UC Berkeley)
- Computational Photography - James Hays (Brown University)
- Computational Photography - Derek Hoiem (UIUC)
- Convolutional Neural Networks for Visual Recognition - Fei-Fei Li and Andrej Karpathy (Stanford University)
- 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
- Multiple View Geometry
- Image Manipulation and Computational Photography - Alexei A. Efros (UC Berkeley)
- Computational Photography - Alexei A. Efros (CMU)
- Computational Photography - Derek Hoiem (UIUC)
- Computational Photography - Irfan Essa (Georgia Tech)
- Introduction to Visual Computing - Kyros Kutulakos (University of Toronto)
- Computational Photography - Kyros Kutulakos (University of Toronto)
- Statistical Learning Theory and Applications - Tomaso Poggio, Lorenzo Rosasco, Carlo Ciliberto, Charlie Frogner, Georgios Evangelopoulos, Ben Deen (MIT)
- 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 - Charles Isbell, Michael Littman (Georgia Tech)
- Machine Learning for Computer Vision - Rudolph Triebel (TU Munich)
- Convex Optimization II - Stephen Boyd (Stanford University)
- Multiple View Geometry
- Machine Learning for Computer Vision - Rudolph Triebel (TU Munich)
- Optimization at MIT - (MIT)
- Computational Photography - Derek Hoiem (UIUC)
- Computer Vision - Steve Seitz (University of Washington)
- Image Manipulation and Computational Photography - Alexei A. Efros (UC Berkeley)
- Digital & Computational Photography - Fredo Durand (MIT)
- Practical Machine Learning - Michael Jordan (UC Berkeley)
- Computer Vision - Rob Fergus (NYU)
- Computational Photography - Rob Fergus (NYU)
- Introduction to Image Processing - Rich Radke (Rensselaer Polytechnic Institute)
Datasets

Programming Languages

C++ 7 Matlab 3 Python 2 Jupyter Notebook 1 MATLAB 1 C 1

Categories

Software 505 Datasets 116 Tutorials and talks 99 Courses 60 Resources for students 34 Books 23 Papers 10 Links 5 Blogs 5 Songs 5 Licenses 1 Contributing 1

Sub Categories

Keywords

awesome-list 2 computer-vision 2 motion-estimation 1 3d-reconstruction 1 slam 1 graph-optimization 1 g2o 1 cpp 1 c-plus-plus 1 recurrent-networks 1 neural-network 1 machine-learning 1 face-images 1 deep-networks 1 deep-learning-tutorial 1 deep-learning 1 awesome 1 superpixels 1 superpixel-algorithm 1 opencv 1 image-processing 1 tonemapping 1 tone-mapping 1 reverse-tone-mapping 1 matlab 1 inverse-tonemapping 1 imaging 1 high-dynamic-range 1 hdr-compression 1 hdr 1 camera-response-function 1 stereo-vision 1 opensource 1 multiple-view-geometry 1