Course Identification

Introduction to computer vision

Lecturers and Teaching Assistants

Prof. Ronen Basri, Prof. Michal Irani, Prof. Shimon Ullman, Dr. Shai Bagon
Hodaya Koslowsky, Ganit Kupershmidt, Eyal Naor

Course Schedule and Location

First Semester
Sunday, 14:15 - 16:00

Field of Study, Course Type and Credit Points

Mathematics and Computer Science: Lecture; Elective; Regular; 2.00 points
Life Sciences: Lecture; Elective; Regular; 2.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; Regular; 2.00 points


All courses in the first semester will be held on-line via zoom.


Students in this course are highly encouraged to take the following course:

  • H. Dym, Basic Topics I, which is offered in the first Semester.

In addition, taking courses in the following topics is recommended for people interested in Vision:

  • Machine-Learning
  • Optimization



Language of Instruction


Registration by


Attendance and participation

Required in at least 80% of the lectures

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

The total grade will be based on 40-50% exercises, and 50-60% exam

Evaluation Type

Take-home exam

Scheduled date 1


Estimated Weekly Independent Workload (in hours)



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

Learning Outcomes

Upon successful completion of this course students should be able to:

  1. Demonstrate understanding of basic computer vision problems.
  2. Apply solution algorithms to basic computer vision problems.
  3. Demonstrate understanding of supervised machine learning in the context of computer vision.
  4. Apply deep learning methods to basic computer vision tasks.

Reading List

  1. E. Trucco, A. Verri. Introductory Techniques for 3-D Computer Vision. Prentice Hall, 1998.
  2. D. A. Forsyth, J. Ponce. Computer Vision a Modern Approach. Prentice Hall, 2003.
  3. R. Szeliski, Computer Vision: Algorithms and Applications. This book draft is currently available online.
  4. Rafael C. Gonzalez, R.E.Woods, Ralph C. Gonzalez. Digital Image Processing. Addison-Wesley, 1992 .
  5. R. Hartley, A.Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2000.
  6. Burt, P., and Adelson, E. H., The Laplacian Pyramid as a Compact Image Code. IEEE Transactions on Communication, 31:532-540, 1983.
  7. Goodfellow, I., Bengio, Y. and Courville, A., 2016. Deep learning. MIT press.