Syllabus
Basics 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.
Text Books
Same as Reference
References
1. Simon Prince, Computer Vision: Models, Learning, and Interface, Cambridge University Press
2. Mubarak Shah, Fundamentals of Computer Vision
3. Richard Szeliski, Computer Vision: Algorithms and Applications, Springer, 2010
4. Forsyth and Ponce, Computer Vision: A Modern Approach, Prentice-Hall, 2002
5. Palmer, Vision Science, MIT Press, 1999
6. Duda, Hart and Stork, Pattern Classification (2nd Edition), Wiley, 2000
7. Koller and Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009,
8. Strang, Gilbert. Linear Algebra and Its Applications 2/e, Academic Press, 1980.
Prerequisites: Basic Probability/Statistics, a good working knowledge of any programming language (Python, Matlab, C/C++, or Java), Linear algebra, and vector calculus.
Programming: Python will be the main programming environment for the assignments. For mini-projects, a processing programming language can also be used (strongly encouraged for android application development)