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  • 8:54 PM, Friday, 22 Oct 2021


Course Postgraduate
Semester Sem. II
Subject Code AVD624
Subject Title Computer Vision

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.

Prerequisities: Basic Probability/Statistics, a good working knowledge of any programming language (python, matlab, C/C++, or Java), Linear algebra, 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.

Text Books
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