awesome-computer-vision

A curated list of awesome computer vision resources
https://github.com/jbhuang0604/awesome-computer-vision

Last synced: 11 days ago
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Awesome Lists
Blogs
- Andrej Karpathy blog - Andrej Karpathy
- Computer vision for dummies - Vincent Spruyt
- AI Shack - Utkarsh Sinha
- Computer Vision Talks - Eugene Khvedchenya
- Computer Vision Basics with Python Keras and OpenCV - Jason Chin (University of Western Ontario)
- Learn OpenCV - Satya Mallick
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
- Image Processing and Analysis - Stan Birchfield 2018
- Computer Vision, From 3D Reconstruction to Recognition - Silvio Savarese 2018
- 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 - Linda G. Shapiro 2001
- OpenCV Essentials - Oscar Deniz Suarez, Mª del Milagro Fernandez Carrobles, Noelia Vallez Enano, Gloria Bueno Garcia, Ismael Serrano Gracia
- Computer Vision: A Modern Approach (2nd edition) - David Forsyth and Jean Ponce 2011
- 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
Courses
- Learning from Data - Yaser S. Abu-Mostafa (Caltech)
- 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)
- Spring 2016 - fall2016/) - Kristen Grauman (UT Austin)
- 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)
- Digital & Computational Photography - Fredo Durand (MIT)
- 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)
- 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)
- Convex Optimization II - Stephen Boyd (Stanford University)
- Convex Optimization - Stephen Boyd (Stanford University)
- Computational Photography - Derek Hoiem (UIUC)
- Computational Photography - Irfan Essa (Georgia Tech)
- Computational Photography - James Hays (Brown University)
- Image Manipulation and Computational Photography - Alexei A. Efros (UC Berkeley)
- 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
- Computational Photography - Alexei A. Efros (CMU)
- 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)
- Methods for Applied Statistics: Unsupervised Learning - Lester Mackey (Stanford)
- Machine Learning - Charles Isbell, Michael Littman (Georgia Tech)
- Convex Optimization II - Stephen Boyd (Stanford University)
- Multiple View Geometry
- Machine Learning for Computer Vision - Rudolph Triebel (TU Munich)
- Optimization at MIT - (MIT)
- Computer Vision - Steve Seitz (University of Washington)
- Image Manipulation and Computational Photography - Alexei A. Efros (UC Berkeley)
- Practical Machine Learning - Michael Jordan (UC Berkeley)
- Computational Photography - Derek Hoiem (UIUC)
- Introduction to Image Processing - Rich Radke (Rensselaer Polytechnic Institute)
- Computer Vision - Rob Fergus (NYU)
- Computational Photography - Rob Fergus (NYU)
Datasets
- Ground Truth Stixel Dataset
- MPI-Sintel Optical Flow Dataset and Evaluation
- HCI Challenge
- Visual Tracker Benchmark
- Computer Vision Dataset on the web
- Are we there yet? - Which paper provides the best results on standard dataset X?
- ComputerVisionOnline Datasets
- CV datasets
- visionbib
- VisualData
- DAVIS: Densely Annotated VIdeo Segmentation
- SegTrack v2
- Labeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms
- ChangeDetection.net
- Intrinsic Images in the Wild
- Intrinsic Image Evaluation on Synthetic Complex Scenes
- OpenSurface
- Flickr Material Database
- Materials in Context Dataset
- Visual Tracker Benchmark v1.1
- Tracking Manipulation Tasks (TMT)
- CAM2
- ChangeDetection.net
- The PASCAL Visual Object Classes
- SUN Database
- Place Dataset
- Microsoft COCO
- Stanford background dataset
- CamVid
- EPFL Car Dataset
- NYU Car Dataset
- Fine-grained Classification Challenge
- ETHZ Pedestrian Detection
- UCF Sports Action Data Set
- Flickr 30K
- Aerial Image Segmentation - Learning Aerial Image Segmentation From Online Maps
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Sun dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- CV Datasets on the web - CVPapers
- Are we there yet? - Which paper provides the best results on standard dataset X?
- CV datasets
- visionbib
- Labeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms
- Single-Image Super-Resolution: A Benchmark
- Intrinsic Images in the Wild
- Intrinsic Image Evaluation on Synthetic Complex Scenes
- OpenSurface

Programming Languages

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

Categories

Software 545 Datasets 134 Tutorials and talks 73 Courses 58 Awesome Lists 53 Resources for students 30 Books 25 Papers 9 Blogs 6 Songs 5 Links 3 Licenses 1 Pre-trained Computer Vision Models 1

Sub Categories

Keywords

deep-learning 13 awesome-list 12 machine-learning 11 awesome 9 computer-vision 9 papers 5 interpretability 4 paper 3 3d-reconstruction 3 xai 3 face-detection 2 image-translation 2 face-recognition 2 fairness 2 primitives 2 point-cloud 2 privacy-preserving-machine-learning 2 neural-networks 2 computer-graphics 2 interpretable-ai 2 image-processing 2 detection 2 curated-list 2 datasets 2 object-detection 2 face-superresolution 1 face-reconstruction 1 face-retrieval 1 face-generation 1 face-tracking 1 face-alignment 1 video-deblurring 1 stereo-deblurring 1 restoration 1 motion-deblurring 1 motion-blur 1 kernel-estimation 1 image-deconvolution 1 image-deblurring 1 defocus-deblurring 1 defocus-blur 1 deblurring 1 camera-shake 1 burst-deblurring 1 g2o 1 cpp 1 c-plus-plus 1 medical-imaging 1 github 1 generative-adversarial-network 1