Course Identification

Introduction to computer vision
20184031

Lecturers and Teaching Assistants

Prof. Ronen Basri, Prof. Michal Irani, Prof. Shimon Ullman
Dr. Netalee Efrat, Dr. Yoni Kasten, Dr. Assaf Shocher

Course Schedule and Location

2018
First Semester
Sunday, 14:15 - 16:00, Ziskind, Rm 1
05/11/2017

Field of Study, Course Type and Credit Points

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

Comments

N/A

Prerequisites

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

Restrictions

40

Language of Instruction

English

Attendance and participation

Required in at least 80% of the lectures

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

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

Evaluation Type

Examination

Scheduled date 1

11/02/2018
Ziskind, Rm 1
1000-1300
N/A

Scheduled date 2

N/A
N/A
-
N/A

Estimated Weekly Independent Workload (in hours)

N/A

Syllabus

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.

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.

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.

Website

N/A