Simon Fraser University
Engaging the World

Course Outlines

Spring 2026 - CMPT 412 D100

Computer Vision (3)

Class Number: 5468

Delivery Method: In Person

Overview

  • Course Times + Location:

    Jan 5 – Apr 10, 2026: Tue, 2:30–5:20 p.m.
    Burnaby

  • Prerequisites:

    CMPT 361 and MATH 152, both with a minimum grade of C-.

Description

CALENDAR DESCRIPTION:

Computational approaches to image and video understanding in relation to theories, the operation of the human visual system, and practical application areas such as robotics. Topics include image classification, object detection, image segmentation based mostly on deep neural network and to some extent classical techniques, and 3D reconstruction. Also covers state-of-the-art deep neural architectures for computer vision applications, such as metric learning, generative adversarial networks, and recurrent neural networks.

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 analysis (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, while we will focus increasingly more on the latter. The course will be based on lectures and assignments (Python). 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 it is strongly recommended that students have upper division units in Computing courses, or permission of the instructor.

COURSE-LEVEL EDUCATIONAL GOALS:

Topics

  • Camera
  • Features
  • Image stitching
  • Segmentation
  • Object detection
  • Recognition
  • Reconstruction
  • Deep learning

Grading

NOTES:

Coding Assignments (100%)

Materials

MATERIALS + SUPPLIES:

Reference Books

  • Computer Vision: Algorithms and Applications (2nd edition), Richard Szeliski, Springer, 2022
    (downloadable from: http://szeliski.org/Book)

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Department Undergraduate Notes:

The following are default policies in the School of Computing Science. Please check your course syllabus whether the instructor has chosen a different policy for your class, otherwise the following policies apply.
 
  • Students must attain an overall passing grade on the weighted average of exams in the course in order to get a C- or higher.
  • All student requests for accommodations for their religious practices must be made in writing by the end of the first week of classes, or no later than one week after a student adds a course. After considering a request, an instructor may provide a concession or may decline to do so. Students requiring accommodations as a result of a disability can contact the Centre for Accessible Learning (caladmin@sfu.ca).

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit: 


RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.