CSE 527: Introduction to Computer Vision



Fall 2012

Tuesdays and Thursdays 10:30 11:50 am

Academic Building 227


Instructor : Seon Joo Kim


Academic Building 422





Use Blackboard page for course documents and information.


This course is a graduate level introduction to computer vision. Although there is no specic prerequisite
course, I expect the students to have undergraduate level knowledge in linear algebra and probability
as well as programming skills in C++ and Matlab. I will cover general areas of computer vision :
low-level computer vision, geometry, and recognition. Within these areas, some of the topics that will be
covered in the class include : image features, color vision, epipolar geometry, stereo vision, bags of features,
and statistical vision. Students will work on several projects including a semester-long term project.

Class participation (15%), 4 projects (50%), final project (35%).

Computer Vision: Algorithms and Applications (c) 2010, Richard Szeliski, Microsoft Research.
Download from http://szeliski.org/Book/ .

Recommended reference:
Computer Vision: A Modern Approach by David Forsyth and Jean Ponce.