CIS4930/5930 - Syllabus at Florida State University

Visual Perception Modeling and Its Applications

CIS4930/5930
Course Reference # 00083/00084
Spring 2001
Department of Computer Science Florida State University

Class time and location

Monday, Wednesday, and Friday, 10:10-11:00AM at LOV 103.

Instructor

Instructor: Xiuwen Liu
Email: liux@cs.fsu.edu (strongly preferred)
Home page: http://www.cs.fsu.edu/~liux
Office: 102C Carothers Building (MCH) Phone: (850) 644-0050
Office Hours: Monday and Wednesday 3:30-5:00PM and by appointments

Course Home Page

http://www.cs.fsu.edu/~liux/courses/research/index.html
This web page contains the up-to-date information related to this course such as news, announcements, assignments, lecture notes, some useful links, and so on. You are required to visit this web site on a regular basis.

Course Description

This course provides introductions to research methodologies and techniques in computer vision in general and the basic principles of computational modeling of vision and perception, from color perception model, texture model, and models for image segmentation and recognition. It offers opportunities to explore applications of computer vision techniques.

Prerequisites

Some basic knowledge of algorithm designs and some experience in C/C++ programming.

Course Objectives

Upon successful completion of this course of study a student:

has a general knowledge of problems in computer vision,
knows basic problems in computational modeling of visual perception,
understands the principles of current approaches,
understands color perception and its modeling,
understands the basics of texture modeling,
has some experience with research in computer vision and image analysis.

Textbook and Class Materials

Chapters from Computer Vision -- A Modern Approach, Prentice Hall, 2000, by David Forsyth and Jean Ponce (Available at http://www.cs.berkeley.edu/~daf/book.html) and papers from the literature. I have dowloaded all the chapters and they are available at ~liux/public_html/courses/research/textbook or through the web at http://www.cs.fsu.edu/~liux/courses/research/textbook/index.html. If you decide to buy a copy of the book, the textbook is scheduled to publish in Feb. 2001 and you can put an order through Amazon.com at $67.00.

Student Responsibilities

This class is very different from "traditional computer science" classes where established concepts are covered. In this class, many of the problems in computer vision have not been solved and you will be given freedom and choices to explore topics that are interested to you. To do that, you need to get familar with existing approaches and so for this class you are required to attend all the lectures. Unless you obtain prior consent of the instructor, missing classes will be used as bases for attendance grading. Participation of in-class discussions and activities is also required.

Assignments and Projects

Exercises will be given to help you understand the basic concepts and techniques and need not to be turned in. A few homework assignments will be given along the lectures.
Based on your own interest, you can choose to write two research papers and present both of them to the class, or to do a programming project and present and demonstrate your results to the class. A research paper can be a literature review or a survey on a particular topic. A programming project can be an implementation of your own novel idea or an implementation based on a published paper.
There will be NO quizzes or exams for this class.

Grading Policy

Grades will be determined as follows:

Assignment	Points
Attendance	10 %
Class participation	10 %
Homework Assignments	20 %
Research Papers Or Programming Projects	40 %
Presentation(s)	20 %

Grading will be based on the following scale:

Score Grade Score Grade Score Grade

93 and above A 80 to 82.9 B- 67 to 69.9 D+

90 to 92.9 A- 77 to 79.9 C+ 63 to 66.9 D

87 to 89.9 B+ 73 to 76.9 C 60 to 62.9 D-

83 to 86.9 B 70 to 72.9 C- 59.9 and below F

Score	Grade	Score	Grade	Score	Grade
93 and above	A	80 to 82.9	B-	67 to 69.9	D+
90 to 92.9	A-	77 to 79.9	C+	63 to 66.9	D
87 to 89.9	B+	73 to 76.9	C	60 to 62.9	D-
83 to 86.9	B	70 to 72.9	C-	59.9 and below	F

Late Penalties

No late submission is allowed in this class because we need to schedule all the presentations in class in advance.

Submission and Return Policy

All tests/assignments/homework will be returned as soon as possible after grading.

Tentative Schedule

Weeks 1 and 2: Introduction
- General introduction to computer vision.
- Human visual information processing.
- Machine visual information processing.
Weeks 3-4: Color perception modeling and its applications.
Week 5: Linear filters and edge detection.
Week 6: Introduction to visual texture modeling.
Week 7: Spectral histogram as a generic texture feature and Julesz ensembles for textures.
Week 8: Introduction to image sequence analysis.
Week 9: Stereopsis.
Week 10: Spring Break (No Classes).
Week 11: Motion estimation and video sequence analysis.
Week 12: Overview of pattern classification and recognition.
Week 13: Face recognition and facial expression analysis.
Week 14: Recognition based on spectral histograms.
Week 15: Computer vision applications.

Academic Honor Code

Programming assignments/written assignments/quizzes/exams are to be done individually, unless specified otherwise. It is a violation of the Academic Honor Code to take credit for the work done by other people. It is also a violation to assist another person in violating the Code. (See the FSU Student Handbook for penalties for violations of the Honor Code.)

The judgment for the violation of the Academic Honor Code will be done by the TA, the instructor and a third part member (another faculty member in the CS department not involved in this course). Once the judgment is made, the case is close and no arguments from the involved parties will be heard. Examples of cheating behavior include:

discuss the solution for a homework question
copy and modify programs.

penalty for violating the Academic Honor Code: A 0 grade for the particular homework/project/exam and a reduction of one letter grade in the final grade for all parties involved. A report will be sent to the department head for further administrative actions.

Accommodation for Disabilities

Students with disabilities needing academic accommodations should: 1. Register with and provide documentation to the Student Disability Resource Center (SDRC); 2. Bring a letter to the instructor from the SDRC indicating you need academic accommodations. This should be done within the first week of class. This syllabus and other class materials are available in alternative format upon request.