Topics (Tentative)

This course aims for students to (1) understand and apply fundamental mathematical and computational techniques in computer vision and (2) implement basic computer vision applications. Students successfully completing this course will be able to apply a variety of computer techniques for the design of efficient algorithms for real-world applications, such as optical character recognition, face detection and recognition, motion estimation, human tracking, and gesture recognition. The topics covered include image filters, edge detection, feature extraction, object detection, object recognition, tracking, gesture recognition, image formation and camera models, and stereo vision.

Pre-requisites/Eligibility

This course is open for PhD, 1st year MTech and BTech 3rd & 4th year students from the CSE department. Pre-requisites are below.

• Introduction to probability
• Introduction to linear algebra
• Programming experience (for course projects)

Resources/References (Tentative)

• Richard Szeliski. "Computer vision: algorithms and applications". Springer, 2010.
• OpenCV 2 Computer Vision Application Programming Cookbook, by Robert Laganière, Packt Publishing Ltd, 2011.
• David A. Forsyth and Jean Ponce, "Computer Vision: A Modern Approach", first edition, Prentice Hall, 2002.