Texts:

1. Required: Computer Vision: A modern approach, David A. Forsyth and Jean Ponce, Prentice Hall.
2. Other Material: Class notes and papers from the following: IEEE Trans. Patt. Anal. Mach. Intell. and other journals

Office hours: TR 1:30PM-3PM or by appointment.

Grading:

1. Homeworks (biweekly): 35%.
2. Midterm: 35%.
   
3. Final Project: 30%

1. Prerequisites: A familiarity with basic concepts in calculus and linear algebra. A partial list of basic requirements follows. Calculus: Differentiation, chain rule, integration. Linear algebra: Matrix multiplication, inverse, pseudo-inverse.
2. Homeworks/programs will be assigned bi-weekly. If you do not have any prior numerical computing experience, I suggest you use MATLAB for the programs.
3. The midterm will be given approximately at the middle of the semester. The last week of classes will be devoted to project presentations.
4. A set of notes which will evolve with the course can be found here.
5. Here is the link to the homework and computational exercises announcements.

 

 
 
 

Image models and early vision: Image formation, edge detection, texture 

Segmentation: Clustering, model fitting

Recognition: Template matching, correspondence, Hough transforms

Deformable models: Snakes, Active contours