198:534   Computer Vision

Spring 2011

 

 

 

Instructor: Ahmed Elgammal -- email: elgammal - cs

Office: Core 316

 

Office hours: 2:30-4:30 pm  CoRE 316

 

Regular class time: Tue 6:40-9:30pm  SERC 206

 

Class TA: Ali Elqursh:  elqursh at  cs

 

 

 

Detailed Syllabus  

 

Lectures’ slides and other materials

 

 

Overview:

This is a basic graduate-level computer vision course that intends to cover a variety of fundamental computer vision topics to get you acquainted with the field.

 

Topics:

Image Formation: Cameras, Geometric camera models, Calibration, Radiometry, Color.

Early Vision: Linear filters, Edge detection, Texture, Geometry of multiple views.

Mid-level Vision: Motion, Segmentation, and Tracking.

High-Level Vision: Model-based vision, Pose estimation, Appearance-based vision, Object recognition.

 

 

Recommended Background:

Linear algebra and basic statistics. 

Familiarity with Matlab programming

 

Textbooks

 

“Computer Vision Algorithms and Application”

By Richard Szeliski

Springer 2010

http://szeliski.org/Book/

 

"Computer Vision: A Modern Approach"

By David Forsyth and Jean Ponce

Prentice Hall 2002

ISBN 0-13-085198-1

 

 

Other references:

 

 

Course Load

§   Homework assignments: (70%) 5 assignments, which contain Matlab programming.

§   Exams: Midterm (30%)  in the second half of the semester.