Syllabus
The course is an introductory level computer vision course, suitable for graduate students. It will cover the basic topics of computer vision, and introduce some fundamental approaches for computer vision research: Image Filtering, Edge Detection, Interest Point Detectors, Motion and Optical Flow, Object Detection and Tracking, Region/Boundary Segmentation, Shape Analysis, and Statistical Shape Models, Deep Learning for Computer Vision, Imaging Geometry, Camera Modeling, and Calibration. Recent Advances in Computer vision.
Prerequisites: Basic Probability/Statistics, a good working knowledge of any programming language (Python, Matlab, C/C++, or Java), Linear algebra, and vector calculus.
Grading: Assignments and the term project should include explanatory/clear comments as well as a short report describing the approach, detailed analysis, and discussion/conclusion.
Course evaluation: 4 Programming assignments 20% (5% each), Term project 20%, Exam 20%, End Semester 40 %
Text Books
Same as Reference
References
1. Computer Vision: Models, Learning, and Interface, Simon Prince, Cambridge University Press
2. Fundamentals of Computer Vision, Mubarak Shah.
3. Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2010
4. Computer Vision: A Modern Approach, Forsyth and Ponce, Prentice-Hall, 2002
5. Vision Science, Palmer, MIT Press, 1999.
6. Pattern Classification 2nd Edition, Duda, Hart and Stork, Wiley, 2000
7. Probabilistic Graphical Models: Principles and Techniques, Koller and Friedman, MIT Press, 2009
8. Linear Algebra and Its Applications 2/e, Strang, Gilbert. Academic Press, 1980.
Programming: Python will be the main programming environment for the assignments. The following book (Python programming samples for computer vision tasks) is freely available. Python for Computer Vision. For mini-projects, a Processing programming language can be used too (strongly encouraged for android application development)