Awesome Computer Vision: 
A curated list of awesome computer vision resources, inspired by awesome-php.
For a list people in computer vision listed with their academic genealogy, please visit here
Contributing
Please feel free to send me pull requests or email ([email protected]) to add links.
Table of Contents
Awesome Lists
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
- Image Processing and Analysis - Stan Birchfield 2018
- Computer Vision, From 3D Reconstruction to Recognition - Silvio Savarese 2018
OpenCV Programming
Machine Learning
Fundamentals
Courses
Computer Vision
Computational Photography
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
Papers
Conference papers on the web
Survey Papers
Pre-trained Computer Vision Models
Tutorials and talks
Computer Vision
Recent Conference Talks
3D Computer Vision
Internet Vision
Computational Photography
Learning and Vision
Object Recognition
Graphical Models
Machine Learning
Optimization
Deep Learning
Software
External Resource Links
General Purpose Computer Vision Library
Multiple-view Computer Vision
Feature Detection and Extraction
- VLFeat
- SIFT
- David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.
- SIFT++
- BRISK
- Stefan Leutenegger, Margarita Chli and Roland Siegwart, "BRISK: Binary Robust Invariant Scalable Keypoints", ICCV 2011
- SURF
- Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool, "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346--359, 2008
- FREAK
- A. Alahi, R. Ortiz, and P. Vandergheynst, "FREAK: Fast Retina Keypoint", CVPR 2012
- AKAZE
- Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison, "KAZE Features", ECCV 2012
- Local Binary Patterns
High Dynamic Range Imaging
Semantic Segmentation
Low-level Vision
Stereo Vision
Optical Flow
Image Denoising
BM3D, KSVD,
Super-resolution
- Multi-frame image super-resolution
- Pickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis 2008
- Markov Random Fields for Super-Resolution
- W. T Freeman and C. Liu. Markov Random Fields for Super-resolution and Texture Synthesis. In A. Blake, P. Kohli, and C. Rother, eds., Advances in Markov Random Fields for Vision and Image Processing, Chapter 10. MIT Press, 2011
- Sparse regression and natural image prior
- K. I. Kim and Y. Kwon, "Single-image super-resolution using sparse regression and natural image prior", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127-1133, 2010.
- Single-Image Super Resolution via a Statistical Model
- T. Peleg and M. Elad, A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution, IEEE Transactions on Image Processing, Vol. 23, No. 6, Pages 2569-2582, June 2014
- Sparse Coding for Super-Resolution
- R. Zeyde, M. Elad, and M. Protter On Single Image Scale-Up using Sparse-Representations, Curves & Surfaces, Avignon-France, June 24-30, 2010 (appears also in Lecture-Notes-on-Computer-Science - LNCS).
- Patch-wise Sparse Recovery
- Jianchao Yang, John Wright, Thomas Huang, and Yi Ma. Image super-resolution via sparse representation. IEEE Transactions on Image Processing (TIP), vol. 19, issue 11, 2010.
- Neighbor embedding
- H. Chang, D.Y. Yeung, Y. Xiong. Super-resolution through neighbor embedding. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol.1, pp.275-282, Washington, DC, USA, 27 June - 2 July 2004.
- Deformable Patches
- Yu Zhu, Yanning Zhang and Alan Yuille, Single Image Super-resolution using Deformable Patches, CVPR 2014
- SRCNN
- Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution, in ECCV 2014
- A+: Adjusted Anchored Neighborhood Regression
- R. Timofte, V. De Smet, and L. Van Gool. A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution, ACCV 2014
- Transformed Self-Exemplars
- Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja, Single Image Super-Resolution using Transformed Self-Exemplars, IEEE Conference on Computer Vision and Pattern Recognition, 2015
Image Deblurring
Non-blind deconvolution
Blind deconvolution
Non-uniform Deblurring
Image Completion
Image Retargeting
Alpha Matting
Image Pyramid
Edge-preserving image processing
Intrinsic Images
Contour Detection and Image Segmentation
Interactive Image Segmentation
Video Segmentation
Camera calibration
Simultaneous localization and mapping
Tracking/Odometry:
Graph Optimization:
Loop Closure:
Localization & Mapping:
Single-view Spatial Understanding
Object Detection
Nearest Neighbor Search
General purpose nearest neighbor search
Nearest Neighbor Field Estimation
Visual Tracking
Saliency Detection
Attributes
Action Reconition
Egocentric cameras
Human-in-the-loop systems
Image Captioning
Optimization
- Ceres Solver - Nonlinear least-square problem and unconstrained optimization solver
- NLopt- Nonlinear least-square problem and unconstrained optimization solver
- OpenGM - Factor graph based discrete optimization and inference solver
- GTSAM - Factor graph based lease-square optimization solver
Deep Learning
Machine Learning
Datasets
External Dataset Link Collection
Low-level Vision
Stereo Vision
Optical Flow
Video Object Segmentation
Change Detection
Image Super-resolutions
Intrinsic Images
Material Recognition
Multi-view Reconsturction
Saliency Detection
Visual Tracking
Visual Surveillance
Saliency Detection
Change detection
Visual Recognition
Image Classification
Self-supervised Learning
Scene Recognition
Object Detection
Semantic labeling
Multi-view Object Detection
Fine-grained Visual Recognition
Pedestrian Detection
Action Recognition
Image-based
Video-based
Image Deblurring
Image Captioning
Scene Understanding
SUN RGB-D - A RGB-D Scene Understanding Benchmark Suite
NYU depth v2 - Indoor Segmentation and Support Inference from RGBD Images
Aerial images
Aerial Image Segmentation - Learning Aerial Image Segmentation From Online Maps
Resources for students
Resource link collection
Writing
Presentation
Research
Time Management
Blogs
Links
Songs
Licenses
License
To the extent possible under law, Jia-Bin Huang has waived all copyright and related or neighboring rights to this work.
这是一份精选的资源列表，涵盖计算机视觉的学习与工作，包括书籍、课程、论文、软件、数据集、预训练模型、教程和演讲。