Image Analysis with Statistical Models

Prof. Joachim Buhmann, Dr. Cheng Soon Ong - Winter Semester 2008

Course Description

This course will focus on inference with statistical models for image analysis. It discusses Markov random fields for image processing reasons and graphical models are for image understanding. In particular, we will look at the following inference problems

Classical inference
Graphical Models
Bayesian Networks
Markov Random Fields
Conditional Random Fields
Support Vector Machines

We will apply these to image analysis questions such as

Digit recognition
Word recognition
Shape from shading
Image segmentation
Object recognition

Time and Place

Lectures	Wed 8-10, CAB H52
Exercises	Wed 10-11 CAB H52

Exercises

Exercise problems will include theoretical and programming problems. Programming will be done in Matlab. Detailed exemplary solutions will be distributed for all exercises.

Performance Assessment

To obtain a Testat (course attendance confirmation), you will be required to attend exercise classes, turn in problem solutions and achieve 50% of all possible points therein.

Please note: We cannot tell you whether you need a Testat or not, only what the requirements are in order to get one for this course. Please consult with the student's administration in your departement. For computer science students, a Testat is usually not required.

Exam

15 Minute oral exam in English.

Syllabus

Week	Lecture Topics	Lecture Slides	Exercise Sheets	Exercise Solutions	Additional Material
Sep 17th	Introduction	lecture01.pdf	series01.pdf	solution01.pdf	01_intro.pdf
Sep 24th	Logistic Regression, Clustering	lecture02.pdf	series02.pdf	solution02.pdf	lr.tar.gz 02_lr.pdf lr_sol.tar.gz
Oct 1st	Shape from Shading	lecture03.pdf	series03.pdf	solution03.pdf	segmentation.tar.gz Hertzmann and Seitz, 2003 03_clustering.pdf segmentation_sol.tar.gz
Oct 8th	Generic viewpoint assumption	lecture04.pdf	series04.pdf	solution04.pdf	Freeman 1994 04_elimination.pdf laplace.pdf
Oct 15th	Graphical models	lecture05.pdf	series05.pdf	solution05.pdf	Chapter 8 of Bishop available from his book website 05_gm.pdf ancestral_sampling.m
Oct 22nd	Sum product	lecture06.pdf	series06.pdf	solution06.pdf	06_gm.pdf
Oct 29th	MRF	lecture07.pdf	series07.pdf	solution07.pdf	07_mrf.pdf mrf.tar.gz
Nov 5th	Sampling	lecture08.pdf	series08.pdf	solution08.pdf	Rosales et. al. gibbs.tar.gz
Nov 12th	Variational Methods	lecture09.pdf	series09.pdf	solution09.pdf	09_variational.pdf
Nov 19th	SVMs	lecture10.pdf	series10.pdf	solution10.pdf	10_kernels.pdf
Nov 26th	CRFs	lecture11.pdf	series11.pdf	solution11.pdf	11_crf.pdf Sutton and McCallum, 2006
Dec 3rd	Structured SVM, Multiple Kernel Learning	lecture12.pdf	series12.pdf	solution12.pdf	12_kernels.pdf
Dec 10th	CRFs for General Graphs	lecture13.pdf
Dec 17th	Review	lecture14.pdf

Slides contain copyrighted material from various sources and are intended for use in the course only.

Resources

References

C. Bishop. Pattern Recognition and Machine Learning. Springer 2007.
This is an excellent introduction to machine learning that covers most topics which will be treated in the lecture. Contains lots of exercises, some with exemplary solutions. Available from ETH-HDB and ETH-INFK libraries.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001.
The classic introduction to machine learning. Available from ETH-BIB and ETH-INFK libraries.

David J.C. Mackay. Information Theory, Inference and Learning Algorithms. Cambridge University Press, 2003.
Available for free from here.

Carl Edward Rasmussen and Christopher K.I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.

G. Winkler. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. Springer-Verlag, 2003.

David A. Forsyth and Jean Ponce. Computer Vision: A Modern Approach. Prentice Hall, 2002.

Rafael C. Gonzalez and Richard E. Woods. Digital Image Processing. Prentice Hall, 3rd edition, 2007.

Jean Jacod and Philip Protter. Probability Essentials. Springer-Verlag, 2nd edition, 2004.

R. M. Dudley. Real Analysis and Probability. Cambridge University Press, 2002.

A. N. Shiryayev. Probability. Springer-Verlag, 1984.

L. Wasserman. All of Statistics. Springer-Verlag, 2003.

Matlab

The official Matlab documentation is available online at the Mathworks website (also in printable form). If you have trouble accessing Matlab's built-in help function, you can use the online function reference on that page or use the command-line version (type help <function> at the prompt). There are several primers and tutorials on the web, a later edition of this one became the book Matlab Primer by T. Davis and K. Sigmon, CRC Press, 2005.

Contact

Instructors: Prof. J. M. Buhmann, Dr. Cheng Soon Ong
Assistant: Patrick Pletscher