MIT CSAIL 6.869: Advances in Computer Vision Spring 2011
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Class Notes and Slides

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Lecture 1	A Simple Vision System	Lecture notes (1/31/2011) Slides: ppt | pdf
Recitation 1	Matlab tutorial	matlab_tutorial.zip
Lecture 2	Filtering	Lecture notes (2/7/2011) Slides: ppt | pdf
Lecture 3	Pyramids and Scale Space	Lecture notes (2/9/2011) Slides: ppt | pdf
Lecture 4	Statistical image models I	Lecture notes (2/14/2011) Slides: ppt | pdf
Lecture 5	Statistical image models II	Slides: ppt | pdf
Lecture 6	Color	Lecture notes (2/22/2011) Slides: ppt | pdf
Lecture 7	Graphical models and belief propagation	Lecture notes (2/23/2011)
Lecture 8	Texture	Lecture notes (2/28/2011) Slides: ppt | pdf
Lecture 9	Edges and Segmentation	Slides: ppt | pdf
Lecture 10	Markov Random Fields	Lecture notes (3/6/2011) Slides: ppt | pdf
Lecture 11	Cameras, lenses, and calibration	Lecture notes (3/9/2011) Slides: ppt | pdf
Lecture 12	Calibration and Stereo	Lecture notes (3/13/2011) Slides: ppt | pdf
Lecture 13	Shape-from-X	Slides: ppt | pdf
Lecture 14	Image Features	Lecture notes (3/28/2011) Slides: ppt | pdf
Lecture 15	Homographies and RANSAC	Lecture notes (3/30/2011) Slides: ppt | pdf
Lecture 16	Motion estimation I	Slides: pdf
Lecture 17	Motion estimation II	Slides: pdf
Lecture 18	Recursive Filtering	Lecture notes (4/11/2011) Slides: ppt | pdf
Lecture 19	Structure from motion	Slides: ppt | pdf
Lecture 20	Object recognition I	Lecture notes (4/19/2011) Slides: ppt | pdf
Lecture 21	Object recognition II	Slides: ppt | pdf
Lecture 22	Object recognition III	Slides: ppt | pdf
Lecture 23	Writing papers and giving talks	Lecture notes (5/2/2011) Slides: pdf
Lecture 24	Internet vision	Lecture notes (5/2/2011) Slides: pdf
Lecture 25	Scene recognition	Slides: ppt | pdf

 

Books

Computer vision:

• [Sz] Szeliski, Computer Vision: Algorithms and Applications, Springer, 2010 (online draft)
• [HZ] Hartley and Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2004
• [FP] Forsyth and Ponce, Computer Vision: A Modern Approach, Prentice Hall, 2002
• [Pa] Palmer, Vision Science, MIT Press, 1999

Learning:

• [Mi] Mitchel, Machine Learning, McGraw-Hill, 1997
• [DHS] Duda, Hart and Stork, Pattern Classification (2nd Edition), Wiley-Interscience, 2000

Graphical models:

• [KF] Koller and Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009

Resources

Related courses:

• Introduction to Computer Vision, by Michael Black
• Learning-Based Methods in Vision, by Alyosha Efros
• Computer Vision, by Kristen Grauman
• Computer Vision, by Rob Fergus
• Introduction to Computer Vision, by Fei-Fei Li

Other resources:

• The Computer Vision Industry

Code

MATLAB:

• MATLAB documentation
• File Exchange
• Code Vectorization Guide
• Writing Fast MATLAB code
• MATLAB array manipulation tips and tricks

Links to useful code:

• OpenCV
• The Steerable Pyramid
• Representation and Synthesis of Visual Texture, Portilla & Simoncelli
• Berkeley Segmentation
• Pb
• Superpixels
• Structure from Motion for Unordered Image Collections
• Peter Kovesi's Functions for Computer Vision
• SIFT implementation by Andrea Vedaldi
• Affine Covariant Features
• A simple object detector with boosting

Other useful code:

• Code for downloading Flickr images