Resources – Machine Vision & Intelligent Systems Lab

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

Courses

Computer Vision

Computational Photography

Machine Learning and Statistical Learning

Optimization

Books

Computer Vision

OpenCV Programming

Machine Learning

Fundamentals

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

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

Scene Recognition

Object Detection

Semantic labeling

Multi-view Object Detection

Fine-grained Visual Recognition

Pedestrian Detection

Action Recognition

Image Deblurring

Image Captioning