The Ultimate List of 300+ Computer Vision Resources

A curated collection of 300+ awesome computer vision resources including books, courses, papers, tutorials, software and more.

Due to the size of this list, it can be hard to keep up with broken links, so if you come across any, please let me know in the comments section.

Also, if you know of any more awesome computer vision resources than what is on this list, please let me know in the comments section.

Table of Contents


Computer Vision

OpenCV Programming

Machine Learning



Computer Vision

Computational Photography

Machine Learning and Statistical Learning



Conference papers on the web

Survey Papers

Tutorials and talks

Computer Vision

Conference Talks

3D Computer Vision

Internet Vision

Computational Photography

Learning and Vision

Object Recognition

Graphical Models

Machine Learning


Deep Learning


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
  • 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

SLAM community:
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

Image Captioning

  • NeuralTalk – NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.


  • 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

Machine Learning


External Dataset Link Collection

Low-level Vision

Stereo Vision
Optical Flow
Image Super-resolutions

Intrinsic Images

Material Recognition

Multi-view Reconsturction

Visual Tracking

Visual Surveillance

Change detection

Visual Recognition

Image Classification
Scene Recognition
Object Detection
Semantic labeling
Multi-view Object Detection
Fine-grained Visual Recognition
Pedestrian Detection

Action Recognition

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

Resources for students

Resource link collection




Time Management


One Response

  1. Ralph Anzarouth July 27, 2016

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