Simon Fraser University
Engaging the World

Course Outlines

Fall 2019 - CMPT 412 D100

Computational Vision (3)

Class Number: 9001

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 3 – Dec 2, 2019: Tue, 2:30–4:20 p.m.
    Burnaby

    Sep 3 – Dec 2, 2019: Thu, 2:30–3:20 p.m.
    Burnaby

  • Exam Times + Location:

    Dec 12, 2019
    Thu, 3:30–6:30 p.m.
    Burnaby

  • Prerequisites:

    MATH 152, and nine units in Computing upper division courses or permission of the instructor.

Description

CALENDAR DESCRIPTION:

Computational approaches to image understanding will be discussed in relation to theories about the operation of the human visual system and with respect to practical applications in robotics. Topics will include edge detection, shape from shading, stereopsis, optical flow, Fourier methods, gradient space, three-dimensional object representation and constraint satisfaction.

COURSE DETAILS:

Computer vision is the process of automatically extracting information from images and videos. The course covers various aspects of Computer Vision, for example, imaging geometry (camera calibration, stereo, and panoramic image stitching), video surveillance (motion detection and tracking), image segmentation, object recognition, and more. The course teaches both traditional techniques and more recent learning-based approaches such as deep neural networks. The course will be based on lectures and assignments. Students with non-standard backgrounds (such as video art, or the use of imaging in physics and biology) are encouraged to contact the instructor. Prerequisites: MATH 152 and nine units in Computing upper division courses or permission of the instructor. CMPT 307 is highly recommended.

Topics

  • Camera
  • Features
  • Image stitching
  • Photometric stereo
  • Optical flow
  • Face
  • Segmentation
  • Object detection
  • Recognition
  • Reconstruction
  • Deep learning

Grading

NOTES:

Coding Assignments (100%)

Students must attain an overall passing grade on the weighted average of exams in the course in order to obtain a clear pass (C- or better).

Materials

MATERIALS + SUPPLIES:

Computer Vision: Algorithms and Applications,
Richard Szeliski
Springer
2011
781848829343
(Note it's downloadable as PDF from: http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf )

Registrar Notes:

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS