2110433 Computer Vision Semester 2/2553

Description:This course will give you a fundamental introduction to the theory and practice of image and video computing. Students will learn from low-level image processing algorithms to high-level pattern recognition and image understanding concepts. Topics include image formation and representation, binary image processing, feature detection and segmentation, color and shading, texture, object recognition, 3D vision, and dynamic vision. The class will also introduce students to some applications such as machine vision systems for inspection, biomedical image analysis, biometrics, intelligent vehicle, and security and surveillance systems.

Prerequisites: None (However, some programming experience will be useful).

Class Schedule: Every Wednesday 09.00-12.00

Room: Eng 1-305/3 (µÖ¡ 1 Ëéͧ 305/3 µÑé§áµè¾Ø¸·Õè 17 ¾.Â. 53 à»ç¹µé¹ä») (Update 15-11-2010)

Text Books: There will be class materials available either in printout format or in soft files on the class webpage. The following text books are supplemental references: Computer Vision by Linda Shapiro and George Stockman, Prentice Hall, 2001 Computer Vision-A Modern Approach, by David Forsyth and Jean Ponce, 2003 Machine Vision, by Jain, Kasturi, and Schunk. McGraw Hill, 1995. Pattern Classification, by Duda, Hart, and Stork, John Wiley, 2000.

Grading Policy: Grades will be based on exams and a project. Midterm exam 30% Final exam 40% Project 30%

Term Project: Students will be asked to form a team of about 3-5 students (depend on the number of class enrollment). Each team will complete a term project emphasizing on pattern/object recognition. Scores will be based on written report and oral presentations/demo.

Download: Colored version of Lecture05 slides.