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:

• G. Medioni, S.B. Kang “Emerging Topics in Computer Vision”, Prentice Hall
• L. Shapiro, G. C. Stockman “Computer Vision”, Prentice Hall.
• Y. Ma, S. Soatto, J. Koseca, S. S. Sastry “An Invitation to 3D Vision, From Images to Geometric Models” Springer
• O. Faugeras “Three-Dimensional Computer Vision: A Geometric View Point”, MIT press.
• Horn “Robot Vision”, MIT press.
• D. Marr “Vision”, Freeman.

Course Load

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

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