EE295B Advanced Computer Vision
Scheduled on Mon/Wed/Fri  9:00 - 9:50am at Votey Room 361 (3 credits)

1. Description
This course deals with computer vision, that is, the analysis of patterns in visual images of 3D scenes; with the goal of interpreting, understanding, and processing information aspects of artificial/computational vision.  Topics covered will be useful for students interested in modern contents of multimedia computing, image processing, personal robotics, remote sensing, medical imaging, and other industrial applications.

2. Prerequisites
Senior or Graduate Standing, Experience on C/C++ and/or Matlab

3. Instructor
Dr. Yuichi Motai, Assistant Professor of Electrical and Computer Engineering

4. Text book
David Forsyth and Jean Ponce, Computer Vision A Modern Approach, Prentice Hall 2003, ISBN 0130851981
Link to Book Review

5. Evaluation
1) Seminar Presentation (10%+10%):  50 minutes lectures from text books
2) Midterm Examination (30%):  In-class exam covered during lectures
3) Project Demonstration (30%): 30-minute project demonstration with www development
4) Document Report (20%): Final description on the completed project

6. Grading
B-  [-66.6%]
B   [66.6%-75.0%]
B+ [75.0%-83.2%]
A- [83.2%-91.5%]
A   [91.5%-100%]

7. Content Covered
Lecture 1   Introduction of Class Project and Textbook
Lecture 2   Cameras
Lecture 3   Geometric Camera Models
Lecture 4   Geometric Camera Calibration
Lecture 5   Radiometry – Measuring Light
Lecture 6   Sources, Shadows, and Shading
Lecture 7   Color
Lecture 8   Image-based Rendering
Lecture 9   Range Data
Lecture 10 Edge Detection
Lecture 11 Segmentation by Clustering
Lecture 12 Segmentation and Filttering Using Probablistic Methods
Lecture 13 Stereopsis
Lecture 14 Affine Structure from Motion
Lecture 15 Projective Structure from Motion
Lecture 16 Review for Chapters 1-16 for Examination Preparation
Lecture 17 Tracking with Linear Dynamic Models
Lecture 18 Model-based Vision
Lecture 19 Selected Topics on Finding Templates using Classifiers
and Recognition by Relations between Templates


8. Tentative Schedule
Week and days    Mon/ Wed/ Fri/
1   1/19-1/23       No class for Martin Luther Day/ Class organization Lecture 1/ Lecture 2/
2   1/26-1/30       Lecture 3/ Lecture 4/ Lecture 5/
3   2/02-2/06       Lecture 6/ Lecture 7/ Lecture 8/
4   2/09-2/13       Lecture 9/ Lecture 10/ Lecture 11/
5   2/16-2/20       No class for President’s Day/ Lecture 12/ Lecture 13/
6   2/23-2/27       Lecture 14/ Lecture 15/ Lecture 16/
7   3/01-3/05       Lecture 17/ Lecture 18/ Lecture 19/
8   3/08-3/12       Midterm Examination/ No class (Exam return)/ A Project Presentation/
9   3/15-3/19       No class for Spring Recess/ No class for Spring Recess/ No class for Spring Recess/
10  3/22-3/26      Project Assignment/ Project Brain Storm/ Project Supervision/
11  3/29-4/02      Individual Supervision (IS)1/ IS2/ IS3/
12  4/05-4/09      IS4/ IS5/ IS6/
13  4/12-4/16      Informal Discussion (ID)1/ IS7/ IS8/
14  4/19-4/23      ID2/ IS9/ IS10/
15  4/26-4/30      Final Project Presentation (FPP1)/ FPP2/ FPP3/

Average GPA 3.7/4.0
1A&3A-

Instructor’s help 5.0/5.0
Instructor overall 4.0/5.0
Class content 4.0/5.0

Projects Conducted
Gyro-senory Overlaid Images
Network Camera Landmark Tracking
Mobile Camera Becon Tracking
In-dark Near-infared Rodent Tracking