Course Contents

Objectives:

• To understand the fundamentals of research areas, where Neuroscience, Machine learning and Engineering interact in Vision. This course will consist of lectures and is based on the premise that a two-way interchange between neuroscience and vision will be mutually productive. This course focuses on understanding of the fundamental concepts related to feature extraction, pattern analysis, motion analysis, segmentation etc. Knowledge of these concepts is necessary in this field, to explore and contribute to research and further developments in the field of computer vision.

• Feature Extraction
  • Overview of Canny, Corners - Harris & Hessian Affine.


• Image Segmentation
  • Analysis of Graph-Cut


• Motion Analysis
  • Tracking (MOG), Optical Flow.

References

Textbooks

Richard Szeliski, Computer Vision: Algorithms and Applications, Springer-Verlag London Limited 2011.

Computer Vision: A Modern Approach, D. A. Forsyth, J. Ponce, Pearson Education, 2003.

References

Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, Second Edition, Cambridge University Press, March 2004.

K. Fukunaga; Introduction to Statistical Pattern Recognition, Second Edition, Academic Press, Morgan Kaufmann, 1990.

R.C. Gonzalez and R.E. Woods, Digital Image Processing, Addison- Wesley, 1992.

Journals

IEEE-T-PAMI (IEEE Transactions on Pattern Analysis and Machine Intelligence).

IJCV (International Journal of Computer Vision) - Springer.

Lecture Slides

1

Classical Computer Vision

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Tracking Videos