Stat 231: Pattern Recognition and Machine Learning

Stat 238

Vision as Bayesian Inference

Tu/Thurs: 9:30-10:50am Winter 2009, Geology 4635.

www.stat.ucla.edu/~yuille/Courses/UCLA/Stat_238/Stat_238.html.

Course Description

This course models vision as Bayesian Inference. It concentrates on visual tasks such as segmenting images, detecting objects in images, and recognizing objects. Its goal is to describe the state of the art techniques. The handouts consist of copies of the lecture notes and related papers.

Reading Material

Course Notes and Handouts.
Papers and Journal Articles.
Instructor: Prof. Alan Yuille, yuille@stat.ucla.edu, 310-267-5383, office Math Sciences 8967.
Office Hours: Wednesday 4.00-5.00 pm (8967 Math Sciences), or by appointment (email).

Grading Plan: 3 homework assignments (20% each). Term project (40%).

Tentative Schedule.


Introduction to the Course:
Piecewise Smooth Image Models:
Learning MRF models :
            Basic Inference Algorithms.
Distributions with Hidden States:
                   
                   
                    
                    Discriminative Random Fields:

                 Lighting Models
 
notes12.pdf
AdaBoost and SVM
Grammatical Models
POMs
Image Parsing:      .
Hierarchical Models.
 
Review of Course 
 



Lecture	Date	Topics	Reading Materials	Handouts
1	01-06	Introduction to the Course: Statistical Edge Detection	statistical_regions.pdf	chp1.pdf
2	01-08	Piecewise Smooth Image Models: Geman & Geman	segmentation_overview.pdf	chp2.pdf
3	01-13	Learning MRF models : without hidden variables.	GYtics.pdf	chp3.pdf
4	01-15	Basic Inference Algorithms. Iterative, Variational, & MCMC		chp4.pdf
5	01-20	Distributions with Hidden States: Dynamic Programming		chp5.pdf
6	01-22	Hidden Markov Models:	chang_peng_2002_2.pdf	chp6.pdf
7	01-27	Snakes & Region Competition	region_competition_pami.pdf	chp7.pdf
8	01-29	Active Bases (Y. Wu):			ywu.pdf
9	02-03	Discriminative Random Fields: Max-Flow/Min-Cut BP.	paper_siggraph04.pdf	chp9.pdf
10	02-05	Hierarchical Models (SWA)		chp10.pdf
11	02-10	Lighting Models		chp11.pdf
12	02-12	Shape Models	fergus03object.pdf Fergus_ECCV4.pdf hand_cviu00J.pdf	notes10.pdf notes11.pdf
13	02-17	Object Models with Interest Points	C9_lzhu_PAMISUB2007.pdf	notes12.pdf
14	02-19	AdaBoost and SVM		summerschool1.pdf
15	02-24	Grammatical Models
16	02-26	POMs	pom_pami_alan_f.pdf
17	03-03	Image Parsing: .		notesDPA.pdf
18	03-05	Hierarchical Models.	HLLM08pami_Alan.pdf	RCM_sum.pdf
19	03-10	Assorted Topics.
20	03-12	Review of Course

		*Final Project Due 20/March. Hand in to Prof. Yuile. Office or Mailbox*