This course will
cover topics in computer vision, robotics and machine learning, with
the emphasis on mathematical tools, computational principles and more
recent trends in these disciplines. I will cover the preliminaries of
image formation and basic extraction of features from images and image
processing providing the background to the paper. Throughout the
semester we read and review selected papers. Each week one student
will present summary of the assigned paper, followed by a discussion
of the strenghts and weaknesses of the paper. The homeworks will be
comprised of implementing the basic technique covered in the paper in
Matlab.
Grading: Homeworks (about every 2 weeks) + Project (optional)
70% Presentations 30%
Prerequisites: linear algebra, calculus
Lecture Materials: Lecture notes
Recommended Textbooks:
Invitation to 3D Vision:
From Images to Geometric Models: Y. Ma, S. Soatto, J. Kosecka and S.
Sastry (for part I of the course)
Computer Vision: A Modern Approach: D. Forsythe and J. Ponce, Prentice-Hall,
2003
Introductory Techniques for 3D computer Vision. E. Trucco and A. Verri,
Prentice-Hall, 1998
Required Software:
Matlab
Quiz
A short quiz on the prerequisites
(not for grade) will be posted here (shortly).
Announcements
The class will not meet on January, 25th.