This course introduces the fundamental concepts of vision with emphasis on computer science and engineering. In particular, the course covers the image formation process, image representation, feature extraction, stereopsis, motion analysis, 3D parameter estimation and applications. A vision laboratory is available where students can gain practical experience.

Specific topics to be covered in this course include the following.

• Introduction
• Image formation
• Image representation
• Feature detection
• Stereopsis
• Motion analysis
• Example application systems
• Additional topics as time permits

General prerequisite; LE/EECS 2030 3.00 or LE/EECS 1030 3.00; SC/MATH 1025 3.00; SC MATH 1310 3.00, LE/EECS 2031 3.00. (NOTE: The General Prerequisite is a cumulative GPA of 4.50 or better over all major EECS courses. EECS courses with the second digit “5” are not major courses.)

It also is recommended that students enter this course with a good working knowledge of the calculus of several variables and linear algebra. Familiarity with linear systems theory (e.g., EECS 3451, formerly COSC 4451 and CSE 3451), comfort with elementary manipulation of complex variables and previous experience equivalent to a university level introduction to physics course also would be of value. If in doubt, then consult with the instructor.

• Richard P. Wildes
  • Office: LAS 3032
  • Office hours: MF 10:30AM-noon
  • Email: wildes@cse.yorku.ca

• Soo Kang
  • Office: LAS 3002
  • Office hours: N/A
  • Email: kangsoo@cse.yorku.ca

• Hakki Can Karaimer
  • Office: LAS 2052
  • Office hours: N/A
  • Email: karaimer@cse.yorku.ca

In order to ensure timely responses to e-mails, please include EECS4422/5323 in the e-mail subject line and include your CSE account number and student number in the body of the e-mail. E-mails lacking such information are unlikely to receive timely or useful response.

The required textbook for this course is

Computer Vision Algorithms and Applications by Richard Szeliski, Springer, 2011.

Errata for the textbook is available here.

This text is available at the York University Bookstore in York Lanes. Also, a copy is on reserve at the Steacie Library on campus.

The workload associated with this course is as follows.

• Lectures: Students will be held responsible for all material covered in lectures. Lecture notes will be posted incrementally on this website; see the Schedule link on the side bar to this page.
• Assigned Readings: Students will be held responsible for all material assigned as reading in the Textbook. Additional reading may be required and copies of relevant material will be made available, as necessary. Reading assignments will be posted incrementally on this website; see the Schedule link on the side bar to this page.
• Labs: Students will be held responsible for all material presented in labs. Labs will provide students with hands-on activities that complement the lecture and reading materials. Documentation of the lab facilities are available here. Note that to make use of the cameras, lenses and tripods during non-scheduled lab times, you need to check them out from the lab monitor.
• Assignments: Two assignments for the students to complete and hand in will be required; details will be posted incrementally on this website; see the Schedule link on the side bar to this page.
• Tests: The only test associated with this course will be a mid-term exam to be given in class, closed book.
• Project: Students will be expected to complete a “hands on” computer vision project. Details can found under the Project link in the side bar.

• Explain the basic terms, concepts and applications of computer vision, including reference to at least one real-world system.
• Apply basic mathematical techniques to solve problems in computer vision.
• Develop software to solve problems in computer vision.
• Analyze the effects of noise in computer vision algorithms and use appropriate techniques to reduce its effects

The weight distribution of the course components is as follows:

• 30% - Assignments: 2 assignments; 15% each.
• 33% - Tests: 1 mid-term exam
• 37% - Project: 4 components
  • 2% - White Paper
  • 11% - Proposal
  • 5% - Site Visit
  • 19% - Final Demo

Each piece of work will be assigned a numeric grade. A final numeric grade will be computed using the weighting given above. The final letter grade will be determined from the numeric score using the standard Computer Science and Engineering mapping.