Computer Vision Course Syllabus – Fall 2016

Projects and Lectures pages have been created. You have your Homework #0 assigned.

During the course of this lecture, you will be doing 3 projects. In the projects, you must use CMake + OpenCV with C++. You can use Matlab for learning the subject, however, Matlab is not allowed in the delivered projects.

Instructor: M. Furkan Kıraç
Office: Engineering Building 219
Phone: 216 564 9542

Office Hours:

Arranged upon request on Mondays (Room EF.219)

Assistant: Mahir Atmış (
13:00-15:00 on Fridays (Room EF.414B)

Course Description
The goal of this course is to introduce the student to the fundamental concepts, mathematical tools and important algorithms that are used for the 3D analysis of images and video. Topics to be covered include cameras and projections, feature detection and matching, stereo vision and multiple view geometry, depth estimation, 3D structure from motion, and object tracking. Some image processing and machine vision concepts will also be introduced to broaden the understanding of computer vision problem solving.

Recommended Books

  • Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2010.
  • Introductory Techniques for 3D Computer Vision, Emanuele Trucco and Alessandro Verri, Prentice-Hall 1998.
  • Computer Vision: A Modern Approach, David A. Forsyth and Jean Ponce, Prentice-Hall, 2002.
  • Learning OpenCV, Gary Bradski and Adrian Kaehler, O’Reilly, 2008.
  • OpenCV 2 Computer Vision Application Programming Cookbook, Robert Laganière, Packt Publishing, 2011.
  • Mastering OpenCV with Practical Computer Vision Projects, Daniel Lélis Baggio, et al., Packt Publishing, 2012.

4 Short Projects: 15% each (total of 60%)
Projects will be done by using CMake + OpenCV + (C++ or Python). MATLAB is not allowed for the projects.

Midterm Exam: 40% (at the very end of the course)

Short Projects: Late submissions are not accepted. Copying answers from others’ work is not permitted.
Midterm Exam: At least 3 of the 4 Short Projects must be turned in by the due date in order to qualify for the Midterm Exam. A make-up will be given for the Midterm Exam for the ones with a proper legally validated excuse.
Attendance: Regular attendance to lectures is strongly encouraged but not mandatory.
Academic Honesty: Any form of cheating will result in a disciplinary action.