Visual Perception Modeling and Its Applications
Course Reference # 00083/00084
Department of Computer Science
Florida State University
Class time and location
Monday, Wednesday, and Friday, 10:10-11:00AM at LOV 103.
Instructor: Xiuwen Liu
Office: 102C Carothers Building (MCH)
Phone: (850) 644-0050
Office Hours: Monday and Wednesday 3:30-5:00PM and by appointments
Course Home Page
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.
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.
Some basic knowledge of algorithm designs and some experience in
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
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
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.
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.
Grades will be determined as follows:
|Class participation||10 %|
|Homework Assignments||20 %|
Or Programming Projects
Grading will be based on the following scale:
|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|
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
- 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:
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.
discuss the solution for a homework question
copy and modify programs.
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.
© 2001, Florida State University.
Updated on January 3, 2000.