General Information

This course will cover basic topics in Computer Vision, Image Processing, and Biological Vision, including basic Fourier analysis, 3D shape recovery from stereo images, motion and video analysis, illumination, and object recognition.

Class Time and Location

Semester Dates

Nov 4, 2018 - Feb 3, 2019
No class on Nov 25, 2018. (The Computer Vision Day)
No class on Dec 2, 2018. (Hanukkah)

Lecture

Sunday 14:15 to 16:00
Room 1, Ziskind Building. (map)

Prerequisites

Proficiency in Python

All programming assignments will be in Python (and use numpy). There is a tutorial here for those who aren't familiar with Python.

College Calculus, Linear Algebra

Students in this course are highly encouraged to take the course "Basic Topics I" by Prof. Harry Dym.

Machine Learning and Optimization

Students in this course are encouraged to take courses in Machine Learning and Optimization.

Grading Policy

Exercises: 40-50%
Final Exam: 50-60%

References

1. Emanuele Trucco, Alessandro Verri. Introductory Techniques for 3-D Computer Vision. Prentice Hall, 1998. ISBN: 0132611082. (location in library)
2. D. A. Forsyth, J. Ponce. Computer Vision a Modern Approach. Prentice Hall, 2003.
3. Rafael C. Gonzalez, R.E.Woods, Ralph C. Gonzalez. Digital Image Processing. Addison-Wesley, 1992 .
4. R. Hartley, A.Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2000. (some parts of this book are available online)
5. R. Szeliski, Computer Vision: Algorithms and Applications. (this book draft is currently available online)
6. R. Hartley, A.Zisserman., Multiple View Geometry - Tutorial. CVPR (1999).
7. Burt, P., and Adelson, E. H., The Pyramid as a Compact Image Code. IEEE Transactions on Communication, COM-31:532-540 (1983).