Computer Vision (Even Sem 2019)

Course Code:  ICVI630E/ICVI240E/ICVI840E
Class Room: 5155 CC3
Schedule: Class (Wednesday 11:15AM to 1:15PM , Friday 6:00 PM to 7:00PM)
TAs: Sudhakar Mishra, Shrikant Malviya, Rohit Mishra, Punit Singh
Google Classroom:
Classroom Code: 9dhchcy


Syllabus: cv_syllabus.pdf
Lectures: S.N. Date Title PPT Reading Material Video
1 Introduction to the course Video
2 Eye and Brain PPT [1] [2] [3] [4]
3 Low Level Vision Process PPT [1]
4 Intermediate-Level Visual Processing PPT [1]
5 The Camera PPT
6 The Camera, Light and Color PPT1 PPT2 PPT3
7 Introduction to Signal and Image Processing PPT
8 Introduction to Signal and Image Processing PPT
9 Object Recognition: History and Overview PPT
10 Object Recognition: History and Overview, Conceptual Issues PPT1 PPT2
11 Bag-of-features models PPT
12 Convolutional Neural Networks PPT [1]
13 CNN Hands-on PPT [1]
14 Camera Caliberation PPT
15 Geometric Vision PPT
16 Triangulation and Epipolar Geometry PPT
17 Stereo Vision PPT
18 Correspondence Problem PPT
19 Structure from Motion PPT
20 Bumblebee PPT [1] [2]
21 Motion and Optical Flow PPT
Reference Books:
  1. Computer Vision: Algorithms and Applications, Richard Szeliski, Springer-Verlag London Limited 2011.
  2. Computer Vision: A Modern Approach, D. A. Forsyth, J. Ponce, Pearson Education, 2003.
  3. Vision Science: Photons to Phenomenology, MIT Press, Cambridge, 1999.
  4. Handbook of Computer Vision, Vol.1, Vol.2, Vol.3 : Bernd Jahne, Horst Haubecker, and Peter Geibler (Eds.), Academic Press, London, 1999.
  5. Siegelbaum, Steven A., and A. J. Hudspeth. Principles of neural science. Eds. Eric R. Kandel, James H. Schwartz, and Thomas M. Jessell. Vol. 4. New York: McGraw-hill, 2000.
  6. Purves, D. et al (2008) Neuroscience 4th edition. Sinauer Associates, Sunderland, MA