Courses
Computer Vision
- 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)
- Computer Vision – Steve Seitz (University of Washington)
- Visual Recognition Spring 2016, Fall 2016 – Kristen Grauman (UT Austin)
- 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)
- 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 Pascal Fua (EPFL):
- Computer Vision 1 Carsten Rother (TU Dresden):
- Computer Vision 2 Carsten Rother (TU Dresden):
- Multiple View Geometry Daniel Cremers (TU Munich):
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)
- Computational Photography – Irfan Essa (Georgia Tech)
- 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)
- 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)
- 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
- 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)
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
OpenCV Programming
- Learning OpenCV: Computer Vision with the OpenCV Library – Gary Bradski and Adrian Kaehler
- Practical Python and OpenCV – Adrian Rosebrock
- OpenCV Essentials – Oscar Deniz Suarez, Mª del Milagro Fernandez Carrobles, Noelia Vallez Enano, Gloria Bueno Garcia, Ismael Serrano Gracia
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
- Gaussian processes for machine learning – Carl Edward Rasmussen and Christopher K. I. Williams 2005
- Learning From Data– Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin 2012
- Neural Networks and Deep Learning – Michael Nielsen 2014
- Bayesian Reasoning and Machine Learning – David Barber, Cambridge University Press, 2012
Fundamentals
- Linear Algebra and Its Applications – Gilbert Strang 1995
Tutorials and talks
Computer Vision
- Computer Vision Talks – Lectures, keynotes, panel discussions on computer vision
- The Three R’s of Computer Vision – Jitendra Malik (UC Berkeley) 2013
- Applications to Machine Vision – Andrew Blake (Microsoft Research) 2008
- 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
- CVPR 2015 – Jun 2015
- ECCV 2014 – Sep 2014
- CVPR 2014 – Jun 2014
- ICCV 2013 – Dec 2013
- ICML 2013 – Jul 2013
- CVPR 2013 – Jun 2013
- ECCV 2012 – Oct 2012
- ICML 2012 – Jun 2012
- CVPR 2012 – Jun 2012
3D Computer Vision
- 3D Computer Vision: Past, Present, and Future – Steve Seitz (University of Washington) 2011
- Reconstructing the World from Photos on the Internet – Steve Seitz (University of Washington) 2013
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
Computational Photography
- 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
- Revealing the Invisible – Frédo Durand (MIT) 2012
- Overview of Computer Vision and Visual Effects – Rich Radke (Rensselaer Polytechnic Institute) 2014
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
- Object Recognition – Larry Zitnick (Microsoft Research)
- 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
Machine Learning
- A Gentle Tutorial of the EM Algorithm – Jeff A. Bilmes (UC Berkeley) 1998
- Introduction To Bayesian Inference – Christopher Bishop (Microsoft Research) 2009
- Support Vector Machines – Chih-Jen Lin (National Taiwan University) 2006
- Bayesian or Frequentist, Which Are You? – Michael I. Jordan (UC Berkeley)
Optimization
- Optimization Algorithms in Machine Learning – Stephen J. Wright (University of Wisconsin-Madison)
- Convex Optimization – Lieven Vandenberghe (University of California, Los Angeles)
- 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
- 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
- Deep Learning for Computer Vision – Rob Fergus (NYU/Facebook Research)
- High-dimensional learning with deep network contractions – Stéphane Mallat (Ecole Normale Superieure)
- Graduate Summer School 2012: Deep Learning, Feature Learning – IPAM, 2012
- Workshop on Big Data and Statistical Machine 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)
Datasets
External Dataset Link Collection
- CV Datasets on the web – CVPapers
- Are we there yet? – Which paper provides the best results on standard dataset X?
- Computer Vision Dataset on the web
- Yet Another Computer Vision Index To Datasets
- ComputerVisionOnline Datasets
- CVOnline Dataset
- CV datasets
- visionbib
- VisualData
Low-level Vision
Stereo Vision
- Middlebury Stereo Vision
- The KITTI Vision Benchmark Suite
- LIBELAS: Library for Efficient Large-scale Stereo Matching
- Ground Truth Stixel Dataset
Optical Flow
- Middlebury Optical Flow Evaluation
- MPI-Sintel Optical Flow Dataset and Evaluation
- The KITTI Vision Benchmark Suite
- HCI Challenge
Video Object Segmentation
Change Detection
- Labeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms
- ChangeDetection.net
Image Super-resolutions
Intrinsic Images
- Ground-truth dataset and baseline evaluations for intrinsic image algorithms
- Intrinsic Images in the Wild
- Intrinsic Image Evaluation on Synthetic Complex Scenes
Material Recognition
Multi-view Reconsturction
Saliency Detection
Visual Tracking
- Visual Tracker Benchmark
- Visual Tracker Benchmark v1.1
- VOT Challenge
- Princeton Tracking Benchmark
- Tracking Manipulation Tasks (TMT)