CSE 6390D/PSYC 6225A 3.0(F) 2010 Computational Modeling of Visual Perception

Instructor Information: James H. Elder 0003G Computer Science and Engineering Building
tel: (416) 736-2100 ext. 66475 fax: (416) 736-5857
email: jelder@yorku.ca website: www.yorku.ca/jelder

General Description:

The goal of this course is to provide a framework and computational tools for modeling visual inference, motivated by interesting examples from the recent literature.  Models may be realized as algorithms to solve computer vision problems, or may constitute theories of visual processing in biological systems.  The foundation of the course is a treatment of visual processing as a problem of statistical estimation and inference, grounded in the ecological statistics of the visual world.

Syllabus

Lectures:

  1. Probability & Bayesian Inference
  2. Probability Distributions
  3. Non-Parametric Modeling
  4. Expectation-Maximization
  5. Subspace Models
  6. Linear Regression
  7. Linear Models for Classification
  8. Kernel Methods
  9. Sparse Kernel Machines
  10. Graphical Models
  11. Sampling

Assignments:

 

Guidelines for Application Paper Presentations:

For Everyone:

Everyone should read the paper prior to the presentation and be prepared to discuss it.

For the Presenter:

Approximate Schedule:

Week

Date

Topic

Required Readings

Additional Readings

Application Paper (Presenter)

1

M Sept 13
W Sept 15

Probability & Bayesian Inference
Probability Distributions & Parametric Modeling

Bishop Ch 1.1-1.2.5 (29 pages)
Bishop Ch 2.1-2.3 (skip 2.3.5) (43 pages)

Pearl Ch 1.4-1.6, 2
Howson & Urbach 1991
Prince Ch 1-4
Duda Ch 3.1-3.5

 

2

M Sept 20
W Sept 22

Probability Distributions & Parametric Modeling (cntd.)
Non-Parametric Modeling

 

Bishop Ch 2.5 (7 pages)

 

Duda Ch 4.1-4.5

Comaniciu & Meer 2002 (Ron Tal)

3

M Sept 27
W Sept 29

Expectation Maximization

Prince Ch 5 (11 pages)
Prince Ch 6.1-6.5, 6.8 (24 pages)

Bishop Ch 9

Stauffer & Grimson 1998 (Paria Mehrani)
Weber & Perona 2000

4

M Oct 4
W Oct 6

Linear Subspace Models

Prince Ch 6.6-6.7, 6.9 (12 pages)
Bishop Ch 12 (40 pages)

Duda Ch 10.13-10.14

Tenenbaum et al 2000
Roweis & Saul 2000 (Abdel-Hamid Ossama)

 

M Oct 11
W Oct 13

Reading Week

 

 

 

5

M Oct 18
W Oct 20

Linear Regression

Bishop Ch 3 (36 pages)

Prince Ch 7.1-7.2

Moghaddam 2002
Cremers 2003 (Junjie Zhang)

6

M Oct 25
W Oct 27

Linear Classifiers

Bishop Ch 4.1-4.3 (34 pages)

Duda 5.1-5.8

Belhumeur et al 1997 (Brian Kim)
Martin et al 2004 (Tareq Mohammad Adnan) - moved to Nov 1

7

M Nov 1
W Nov 3

Non-Linear Regression & Classification

Bishop Ch 6 (29 pages)

Prince Ch 7.3-7.4

Toyama & Blake 2001
Grochow et al 2004 (Anna Topol)

8

M Nov 8
W Nov 10

Sparse Kernel Machines

Bishop 7.1 (20 pages)

 

Agarwal & Triggs 2006 (Eduardo Corral Soto)
Zhang et al 2007

9

M Nov 15
W Nov 17

Graphical Models:  Introduction

Bishop Ch 8.1-8.3 (34 pages)

 

Freeman et al 2000 (Ravi Persad)
Shi & Malik 2000 (Xiwen Chen)

10

M Nov 22
W Nov 24

Graphical Models:  Inference

Bishop Ch 8.4 (25 pages)

 

Boykov & Funka-Lea 2006 (Chao Luo)
He et al 2004 (Anthony Calce)

11

M Nov 29
W Dec 1

Graphical Models:  Applications

Prince Ch 10-11 (56 pages)

 

Frey & Jojic 2005
Szeliski et al 2008
Friedman et al 2004 (Wendy Ashlock)

12

M Dec 6
W Dec 8

Sampling Methods

Bishop Ch 11 (32 pages)

 

Zhu 1999
Yuille & Kersten 2006 (Calden Wloka)

Main Texts:

Additional Readings: