Course schedule :: Annoucements & Resources
The course is an introduction to 2D and 3D computer vision. Topics include: cameras models, the geometry of multiple views; shape reconstruction methods from visual cues: stereo, shading, shadows, contours; low-level image processing methodologies such as edge detection, feature detection; mid-level vision techniques (segmentation and clustering); Basic high-level vision problems: face detection, object and scene recognition, object categorization, and human tracking.
- Computer Vision, A Modern Approach, by D.A. Forsyth and J. Ponce, Prentice Hall, 2003.
- Multiple View Geometry in Computer Vision, by R. Hartley and A. Zisserman, Academic Press, 2002
Linear algebra; some knowledge of probability & statistics; MATLAB programming experience is desirable but not required.
1 mid term exam
Attendance & participation: 5%
Homeworks: 4 homeworks (10% each)
Exam: 1 mid term exam (10%)
Project: progress report (%5), final report(30%), presentation (10%)
Homework late policy: 50% if one day late; zero credit if more than one day.
Project late policy: 25% if one day late; 50% if two days late; zero credit if more than two days