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 (jbhuang@vt.edu) 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 * List of
Computer Vision models These models are trained on custom
objects
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 * Spatially
variant non-blind deconvolution * Handling
Outliers in Non-blind Image Deconvolution * Hyper-Laplacian
Priors * From
Learning Models of Natural Image Patches to Whole Image Restoration
* Deep Convolutional Neural
Network for Image Deconvolution * Neural
Deconvolution
Blind deconvolution * Removing
Camera Shake From A Single Photograph * High-quality
motion deblurring from a single image * Two-Phase
Kernel Estimation for Robust Motion Deblurring * Blur
kernel estimation using the radon transform * Fast
motion deblurring * Blind
Deconvolution Using a Normalized Sparsity Measure * Blur-kernel
estimation from spectral irregularities * Efficient
marginal likelihood optimization in blind deconvolution * Unnatural L0
Sparse Representation for Natural Image Deblurring * Edge-based
Blur Kernel Estimation Using Patch Priors * Blind
Deblurring Using Internal Patch Recurrence
Non-uniform Deblurring * Non-uniform
Deblurring for Shaken Images * Single
Image Deblurring Using Motion Density Functions * Image
Deblurring using Inertial Measurement Sensors * Fast
Removal of Non-uniform Camera Shake
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
Licenses
License
To the extent possible under law, Jia-Bin Huang has waived all copyright
and related or neighboring rights to this work.
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