Stat 231: Pattern Recognition and Machine Learning

Stat 238

Vision as Bayesian Inference

Tu/Thurs: 9:30-10:50am Fall 2007, Math/Sciences 7608.

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:
Statistical Region & Edges:
First Generation Image Models:
            First Generation Image Models (cont):
Region Competition:
                   
                   
                    
                    Hierarchical Models:

                 Learning: AdaBoost
 
notes10.pdf
Hierarchical Models of Shape
Grammatical Models
Learning
Thanksgiving
Image Parsing: 
 
Assorted Topics 
Review of Course



Lecture	Date	Topics	Reading Materials	Handouts
1	09-27	Introduction to the Course: Statistical Regions & Edges	statistical_regions.pdf	notes1.pdf
2	10-02	Statistical Region & Edges: Continued	statistical_edges.pdf	ProbReviewGT.pdf noteslextras12.pdf
3	10-04	First Generation Image Models: Mumford-Shah et al.	segmentation_overview.pdf	notes2.pdf
4	10-09	First Generation Image Models (cont): Snakes and Learning.	Pietra.pdf	notes3.pdf
5	10-11	Region Competition: Multiple Region Models	region_competition_pami.pdf	notes4.pdf
6	10-16	Discriminative Random Fields	kumar_discriminate.pdf	notes5.pdf
7	10-18	Max-Flow/Min-Cut and BP	paper_siggraph04.pdf	notes6.pdf
8	10-23	Hierarchical Models:	Segmentation_CVPR00_paper.pdf Segmentation_CVPR01.pdf		notes7.pdf
9	10-25	Hierarchical Models:	A212_jcorso_ipmi2007.pdf C4_jcorso_MICCAI2007.pdf	video00.mpeg4
10	10-30	Learning: SVM		summerschool.pdf
11	11-01	Learning: AdaBoost	viola01rapid.pdf
12	11-06	Lighting Models	svd_ijcv99J.pdf	notes9.pdf
13	11-08	Shape Matching	ShapeMatching.pdf	notes10.pdf
14	11-13	Hierarchical Models of Shape	hand_cviu00J.pdf	notes11.pdf
15	11-15	Grammatical Models	fergus03object.pdf Fergus_ECCV4.pdf C9_lzhu_PAMISUB2007.pdf	notes12.pdf
16	11-20	Learning
	11-22	Thanksgiving .
17	11-27	Image Parsing: .		notes13.pdf
18	11-29	Assorted Topics.		notes14.pdf
19	12-04	Assorted Topics
20	12-06	Review of Course
	12-14	*Final Project Due 14/Dec. Hand in to Prof. Yuile. Office or Mailbox*