Track Awesome Computer Vision Updates Weekly
A curated list of awesome computer vision resources
🏠 Home · 🔍 Search · 🔥 Feed · 📮 Subscribe · ❤️ Sponsor · 😺 jbhuang0604/awesome-computer-vision · ⭐ 17K · 🏷️ Computer Science
Sep 27 - Oct 03, 2021
Visual Recognition / Self-supervised Learning
Apr 19 - Apr 25, 2021
Awesome Lists
Feb 22 - Feb 28, 2021
Pre-trained Computer Vision Models
- List of Computer Vision models (⭐28) These models are trained on custom objects
Feb 01 - Feb 07, 2021
Awesome Lists
Jan 11 - Jan 17, 2021
Awesome Lists
Jan 04 - Jan 10, 2021
Computer Vision
- Computer Vision, From 3D Reconstruction to Recognition - Silvio Savarese 2018
Dec 14 - Dec 20, 2020
Computer Vision
- Image Processing and Analysis - Stan Birchfield 2018
Mar 25 - Mar 31, 2019
Annotation tools
Jan 15 - Jan 21, 2018
Machine Learning and Statistical Learning
- Machine Learning for Computer Vision - Rudolph Triebel (TU Munich)
Blogs / Image Deblurring
- Computer Vision Basics with Python Keras and OpenCV (⭐416) - Jason Chin (University of Western Ontario)
Nov 20 - Nov 26, 2017
External Dataset Link Collection / Nearest Neighbor Field Estimation
May 22 - May 28, 2017
Low-level Vision / Video Object Segmentation
May 08 - May 14, 2017
Computer Vision
- Computer Vision Pascal Fua (EPFL):
- Computer Vision 1 Carsten Rother (TU Dresden):
- Computer Vision 2 Carsten Rother (TU Dresden):
- Multiple View Geometry Daniel Cremers (TU Munich):
Mar 06 - Mar 12, 2017
Machine Learning and Statistical Learning
- (Convolutional) Neural Networks for Visual Recognition - Fei-Fei Li, Andrej Karphaty, Justin Johnson (Stanford University)
Oct 31 - Nov 06, 2016
Computer Vision
- Visual Recognition Spring 2016, Fall 2016 - Kristen Grauman (UT Austin)
- Computer Vision - Bastian Leibe (RWTH Aachen University)
- Computer Vision 2 - Bastian Leibe (RWTH Aachen University)
Oct 24 - Oct 30, 2016
Computer Vision
- Advances in Computer Vision - Antonio Torralba and Bill Freeman (MIT)
Computational Photography
- Computational Photography - Irfan Essa (Georgia Tech)
Machine Learning and Statistical Learning
- Intro to Machine Learning - Sebastian Thrun (Stanford University)
- Machine Learning - Charles Isbell, Michael Littman (Georgia Tech)
Links / Image Deblurring
Oct 03 - Oct 09, 2016
Links / Image Deblurring
Sep 26 - Oct 02, 2016
Low-level Vision / Change Detection
Sep 19 - Sep 25, 2016
Multiple-view Computer Vision
Sep 05 - Sep 11, 2016
Links / Image Deblurring
Aug 15 - Aug 21, 2016
Computer Vision
- Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics - Justin Solomon 2015
Jun 06 - Jun 12, 2016
Visual Tracking / Nearest Neighbor Field Estimation
May 23 - May 29, 2016
Semantic Segmentation
Mar 14 - Mar 20, 2016
Visual Tracking / Image Super-resolutions
Jan 18 - Jan 24, 2016
Computational Photography
- Computer Vision for Visual Effects - Rich Radke (Rensselaer Polytechnic Institute)
- Introduction to Image Processing - Rich Radke (Rensselaer Polytechnic Institute)
Machine Learning and Statistical Learning
- Machine Learning - Andrew Ng (Stanford University)
Object Recognition
- Object Recognition - Larry Zitnick (Microsoft Research)
Machine Learning
- A Gentle Tutorial of the EM Algorithm - Jeff A. Bilmes (UC Berkeley) 1998
Multiple-view Computer Vision
- MinimalSolvers - Minimal problems solver
Contour Detection and Image Segmentation / Edge-preserving image processing
Simultaneous localization and mapping / Loop Closure:
- FabMap: appearance-based loop closure system - also available in OpenCV2.4.11
Links / Image Deblurring
Dec 14 - Dec 20, 2015
Visual Tracking / Nearest Neighbor Field Estimation
Dec 07 - Dec 13, 2015
Visual Tracking / Nearest Neighbor Field Estimation
Nov 23 - Nov 29, 2015
Simultaneous localization and mapping / Tracking/Odometry:
Oct 05 - Oct 11, 2015
Machine Learning and Statistical Learning
- Course on Information Theory, Pattern Recognition, and Neural Networks - David MacKay (University of Cambridge)
External Resource Links
Low-level Vision / Image Completion
Low-level Vision / Image Retargeting
Contour Detection and Image Segmentation / Edge-preserving image processing
Simultaneous localization and mapping / Tracking/Odometry:
Simultaneous localization and mapping / Localization & Mapping:
Image Captioning / Nearest Neighbor Field Estimation
Blogs / Image Deblurring
- Computer Vision Talks - Eugene Khvedchenya
Sep 28 - Oct 04, 2015
Visual Tracking / Nearest Neighbor Field Estimation
Sep 07 - Sep 13, 2015
Object Detection / Localization & Mapping:
Aug 10 - Aug 16, 2015
Computer Vision
- 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
Machine Learning
- Gaussian processes for machine learning - Carl Edward Rasmussen and Christopher K. I. Williams 2005
Recent Conference Talks
- ICML 2013 - Jul 2013
- ICML 2012 - Jun 2012
Computational Photography
- Overview of Computer Vision and Visual Effects - Rich Radke (Rensselaer Polytechnic Institute) 2014
General Purpose Computer Vision Library
High Dynamic Range Imaging
Machine Learning / Nearest Neighbor Field Estimation
Jul 27 - Aug 02, 2015
Simultaneous localization and mapping / Tracking/Odometry:
Blogs / Image Deblurring
- AI Shack - Utkarsh Sinha
Jun 22 - Jun 28, 2015
Deep Learning / Nearest Neighbor Field Estimation
Material Recognition / Image Super-resolutions
Visual Recognition / Pedestrian Detection
Action Recognition / Video-based
Action Recognition / Image Deblurring
Jun 08 - Jun 14, 2015
Resource link collection / Image Deblurring
- Resource collection - Tao Xie (UIUC) and Yuan Xie (UCSB)
Writing / Image Deblurring
- Common mistakes in technical writing - Wojciech Jarosz (Dartmouth College)
Songs / Image Deblurring
Jun 01 - Jun 07, 2015
Computer Vision
- EENG 512 / CSCI 512 - Computer Vision - William Hoff (Colorado School of Mines)
Blogs / Image Deblurring
- Learn OpenCV - Satya Mallick
- Tombone's Computer Vision Blog - Tomasz Malisiewicz
- Computer vision for dummies - Vincent Spruyt
- Andrej Karpathy blog - Andrej Karpathy
May 25 - May 31, 2015
Contour Detection and Image Segmentation / Edge-preserving image processing
Object Detection / Localization & Mapping:
May 18 - May 24, 2015
Computer Vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
Apr 20 - Apr 26, 2015
Deep Learning
- Machine Learning Summer School - Reykjavik, Iceland 2014
- Deep Learning Session 1 - Yoshua Bengio (Universtiy of Montreal)
- Deep Learning Session 2 - Yoshua Bengio (University of Montreal)
- Deep Learning Session 3 - Yoshua Bengio (University of Montreal)
General Purpose Computer Vision Library
Multiple-view Computer Vision
- openMVG: open Multiple View Geometry - Multiple View Geometry; Structure from Motion library & softwares
Simultaneous localization and mapping / SLAM community:
Simultaneous localization and mapping / Tracking/Odometry:
Simultaneous localization and mapping / Graph Optimization:
- GTSAM: General smoothing and mapping library for Robotics and SFM -- Georgia Institute of Technology
Simultaneous localization and mapping / Loop Closure:
Simultaneous localization and mapping / Localization & Mapping:
Optimization / Nearest Neighbor Field Estimation
- 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
Apr 06 - Apr 12, 2015
Recent Conference Talks
- CVPR 2015 - Jun 2015
Mar 23 - Mar 29, 2015
Low-level Vision / Optical Flow
Links / Image Deblurring
Mar 16 - Mar 22, 2015
Computational Photography
- Revealing the Invisible - Frédo Durand (MIT) 2012
Mar 09 - Mar 15, 2015
Machine Learning / Nearest Neighbor Field Estimation
Low-level Vision / 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
Mar 02 - Mar 08, 2015
OpenCV Programming
- OpenCV Essentials - Oscar Deniz Suarez, Mª del Milagro Fernandez Carrobles, Noelia Vallez Enano, Gloria Bueno Garcia, Ismael Serrano Gracia
Feature Detection and Extraction
Intrinsic Images / Edge-preserving image processing
Contour Detection and Image Segmentation / Edge-preserving image processing
Video Segmentation / Edge-preserving image processing
Feb 23 - Mar 01, 2015
OpenCV Programming
- Practical Python and OpenCV - Adrian Rosebrock
Machine Learning
- Bayesian Reasoning and Machine Learning - David Barber, Cambridge University Press, 2012
Machine Learning and Statistical Learning
- Methods for Applied Statistics: Unsupervised Learning - Lester Mackey (Stanford)
- Machine Learning - Andrew Zisserman (University of Oxford)
Feature Detection and Extraction
Visual Tracking / Nearest Neighbor Field Estimation
Intrinsic Images / Image Super-resolutions
Jan 26 - Feb 01, 2015
Fundamentals
- Linear Algebra and Its Applications - Gilbert Strang 1995
Conference papers on the web
Survey Papers
Computer Vision
- The Future of Image Search - Jitendra Malik (UC Berkeley) 2008
- Should I do a PhD in Computer Vision? - Fatih Porikli (Australian National University)
- Graduate Summer School 2013: Computer Vision - IPAM, 2013
Recent Conference Talks
- ECCV 2014 - Sep 2014
- CVPR 2014 - Jun 2014
- ICCV 2013 - Dec 2013
- CVPR 2013 - Jun 2013
- ECCV 2012 - Oct 2012
- CVPR 2012 - Jun 2012
3D Computer Vision
- Reconstructing the World from Photos on the Internet - Steve Seitz (University of Washington) 2013
Machine Learning
- Bayesian or Frequentist, Which Are You? - Michael I. Jordan (UC Berkeley)
Optimization
- Continuous Optimization in Computer Vision - Andrew Fitzgibbon (Microsoft Research)
- Beyond stochastic gradient descent for large-scale machine learning - Francis Bach (INRIA)
- Variational Methods for Computer Vision - Daniel Cremers (Technische Universität München) (lecture 18 missing from playlist)
Deep Learning
- Deep Learning for Computer Vision - Rob Fergus (NYU/Facebook Research)
- High-dimensional learning with deep network contractions - Stéphane Mallat (Ecole Normale Superieure)
External Resource Links
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Computer Vision Algorithm Implementations - CVPapers
- Source Code Collection for Reproducible Research - Xin Li (West Virginia University)
General Purpose Computer Vision Library
Multiple-view Computer Vision
Feature Detection and Extraction
- SIFT
- David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.
- 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
Low-level Vision / Stereo Vision
Low-level Vision / Optical Flow
- Coarse2Fine Optical Flow - Ce Liu (MIT)
Low-level Vision / Image Deblurring
Low-level Vision / Alpha Matting
Low-level Vision / Image Pyramid
Contour Detection and Image Segmentation / Edge-preserving image processing
Interactive Image Segmentation / Edge-preserving image processing
Video Segmentation / Edge-preserving image processing
Simultaneous localization and mapping / SLAM community:
Simultaneous localization and mapping / Localization & Mapping:
Single-view Spatial Understanding / Localization & Mapping:
- Geometric Context - Derek Hoiem (CMU)
- Recovering Spatial Layout - Varsha Hedau (UIUC)
- Geometric Reasoning - David C. Lee (CMU)
- RGBD2Full3D (⭐23) - Ruiqi Guo (UIUC)
Object Detection / Localization & Mapping:
Nearest Neighbor Search / General purpose nearest neighbor search
Nearest Neighbor Search / Nearest Neighbor Field Estimation
Visual Tracking / Image Super-resolutions
External Dataset Link Collection / Nearest Neighbor Field Estimation
- CV Datasets on the web - CVPapers
- Are we there yet? - Which paper provides the best results on standard dataset X?
Low-level Vision / Image Super-resolutions
Intrinsic Images / Image Super-resolutions
Material Recognition / Image Super-resolutions
Multi-view Reconsturction / Image Super-resolutions
Visual Surveillance / Image Super-resolutions
Change detection / Image Super-resolutions
Visual Recognition / Object Detection
Visual Recognition / Image Classification
Visual Recognition / Scene Recognition
Image Captioning / Image Deblurring
Visual Recognition / Semantic labeling
Visual Recognition / Multi-view Object Detection
Visual Recognition / Fine-grained Visual Recognition
Visual Recognition / Pedestrian Detection
Action Recognition / Video-based
Action Recognition / Image Deblurring
Resource link collection / Image Deblurring
- Resources for students - Frédo Durand (MIT)
- Advice for Graduate Students - Aaron Hertzmann (Adobe Research)
- Graduate Skills Seminars - Yashar Ganjali, Aaron Hertzmann (University of Toronto)
- Research Skills - Simon Peyton Jones (Microsoft Research)
Writing / Image Deblurring
- Write Good Papers - Frédo Durand (MIT)
- Notes on writing - Frédo Durand (MIT)
- How to Write a Bad Article - Frédo Durand (MIT)
- How to write a good CVPR submission - William T. Freeman (MIT)
- How to write a great research paper - Simon Peyton Jones (Microsoft Research)
- How to write a SIGGRAPH paper - SIGGRAPH ASIA 2011 Course
- Writing Research Papers - Aaron Hertzmann (Adobe Research)
- How to Write a Paper for SIGGRAPH - Jim Blinn
- How to Get Your SIGGRAPH Paper Rejected - Jim Kajiya (Microsoft Research)
- How to write a SIGGRAPH paper - Li-Yi Wei (The University of Hong Kong)
- How to Write a Great Paper - Martin Martin Hering Hering--Bertram (Hochschule Bremen University of Applied Sciences)
- How to have a paper get into SIGGRAPH? - Takeo Igarashi (The University of Tokyo)
- Good Writing - Marc H. Raibert (Boston Dynamics, Inc.)
- How to Write a Computer Vision Paper - Derek Hoiem (UIUC)
Presentation / Image Deblurring
- Giving a Research Talk - Frédo Durand (MIT)
- How to give a good talk - David Fleet (University of Toronto) and Aaron Hertzmann (Adobe Research)
- Designing conference posters - Colin Purrington
Research / Image Deblurring
- How to do research - William T. Freeman (MIT)
- You and Your Research - Richard Hamming
- Warning Signs of Bogus Progress in Research in an Age of Rich Computation and Information - Yi Ma (UIUC)
- Seven Warning Signs of Bogus Science - Robert L. Park
- Five Principles for Choosing Research Problems in Computer Graphics - Thomas Funkhouser (Cornell University)
- How To Do Research In the MIT AI Lab - David Chapman (MIT)
- Recent Advances in Computer Vision - Ming-Hsuan Yang (UC Merced)
- How to Come Up with Research Ideas in Computer Vision? - Jia-Bin Huang (UIUC)
- How to Read Academic Papers - Jia-Bin Huang (UIUC)
Time Management / Image Deblurring
- Time Management - Randy Pausch (CMU)
Links / Image Deblurring
- The Computer Vision Industry - David Lowe
Jan 19 - Jan 25, 2015
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
- Visual Object and Activity Recognition - Alexei A. Efros and Trevor Darrell (UC Berkeley)
- Computer Vision - Steve Seitz (University of Washington)
- Language and Vision - Tamara Berg (UNC Chapel Hill)
- Convolutional Neural Networks for Visual Recognition - Fei-Fei Li and Andrej Karpathy (Stanford University)
- Computer Vision - Rob Fergus (NYU)
- 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)
- The Three R's of Computer Vision - Jitendra Malik (UC Berkeley) 2013
- Applications to Machine Vision - Andrew Blake (Microsoft Research) 2008
OpenCV Programming
- Learning OpenCV: Computer Vision with the OpenCV Library - Gary Bradski and Adrian Kaehler
Machine Learning
- 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
- Pattern Classification - Peter E. Hart, David G. Stork, and Richard O. Duda 2000
- Machine Learning - Tom M. Mitchell 1997
- Learning From Data- Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin 2012
- Neural Networks and Deep Learning - Michael Nielsen 2014
- Introduction To Bayesian Inference - Christopher Bishop (Microsoft Research) 2009
- Support Vector Machines - Chih-Jen Lin (National Taiwan University) 2006
Computational Photography
- Image Manipulation and Computational Photography - Alexei A. Efros (UC Berkeley)
- Computational Photography - Alexei A. Efros (CMU)
- Computational Photography - Derek Hoiem (UIUC)
- Computational Photography - James Hays (Brown University)
- Digital & Computational Photography - Fredo Durand (MIT)
- Computational Camera and Photography - Ramesh Raskar (MIT Media Lab)
- Courses in Graphics - Stanford University
- Computational Photography - Rob Fergus (NYU)
- Introduction to Visual Computing - Kyros Kutulakos (University of Toronto)
- Computational Photography - Kyros Kutulakos (University of Toronto)
- Reflections on Image-Based Modeling and Rendering - Richard Szeliski (Microsoft Research) 2013
- Photographing Events over Time - William T. Freeman (MIT) 2011
- Old and New algorithm for Blind Deconvolution - Yair Weiss (The Hebrew University of Jerusalem) 2011
- A Tour of Modern "Image Processing" - Peyman Milanfar (UC Santa Cruz/Google) 2010
- Topics in image and video processing Andrew Blake (Microsoft Research) 2007
- Computational Photography - William T. Freeman (MIT) 2012
Machine Learning and Statistical Learning
- 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)
Optimization
- Convex Optimization I - Stephen Boyd (Stanford University)
- Convex Optimization II - Stephen Boyd (Stanford University)
- Convex Optimization - Stephen Boyd (Stanford University)
- Optimization at MIT - (MIT)
- Convex Optimization - Ryan Tibshirani (CMU)
- Optimization Algorithms in Machine Learning - Stephen J. Wright (University of Wisconsin-Madison)
- Convex Optimization - Lieven Vandenberghe (University of California, Los Angeles)
Conference papers on the web
- CVPapers - Computer vision papers on the web
- SIGGRAPH Paper on the web - Graphics papers on the web
- NIPS Proceedings - NIPS papers on the web
- Annotated Computer Vision Bibliography - Keith Price (USC)
3D Computer Vision
- 3D Computer Vision: Past, Present, and Future - Steve Seitz (University of Washington) 2011
Internet Vision
- The Distributed Camera - Noah Snavely (Cornell University) 2011
- Planet-Scale Visual Understanding - Noah Snavely (Cornell University) 2014
- A Trillion Photos - Steve Seitz (University of Washington) 2013
Learning and Vision
- Where machine vision needs help from machine learning - William T. Freeman (MIT) 2011
- Learning in Computer Vision - Simon Lucey (CMU) 2008
- Learning and Inference in Low-Level Vision - Yair Weiss (The Hebrew University of Jerusalem) 2009
Object Recognition
- Generative Models for Visual Objects and Object Recognition via Bayesian Inference - Fei-Fei Li (Stanford University)
Graphical Models
- Graphical Models for Computer Vision - Pedro Felzenszwalb (Brown University) 2012
- Graphical Models - Zoubin Ghahramani (University of Cambridge) 2009
- Machine Learning, Probability and Graphical Models - Sam Roweis (NYU) 2006
- Graphical Models and Applications - Yair Weiss (The Hebrew University of Jerusalem) 2009
Deep Learning
- A tutorial on Deep Learning - Geoffrey E. Hinton (University of Toronto)
- Deep Learning - Ruslan Salakhutdinov (University of Toronto)
- Scaling up Deep Learning - Yoshua Bengio (University of Montreal)
- ImageNet Classification with Deep Convolutional Neural Networks - Alex Krizhevsky (University of Toronto)
- The Unreasonable Effectivness Of Deep Learning Yann LeCun (NYU/Facebook Research) 2014
General Purpose Computer Vision Library
Feature Detection and Extraction
Multiple-view Computer Vision
- OpenGV - geometric computer vision algorithms
Low-level Vision / Optical Flow
Low-level Vision / Image Completion
Low-level Vision / Alpha Matting
Low-level Vision / Edge-preserving image processing
Contour Detection and Image Segmentation / Edge-preserving image processing
Camera calibration / Edge-preserving image processing
Object Detection / Localization & Mapping:
Visual Tracking / Nearest Neighbor Field Estimation
Songs / Image Deblurring