Fall 2023 - CMPT 412 D100
Computer Vision (3)
Class Number: 6797
Delivery Method: In Person
Course Times + Location:
Sep 6 – Oct 6, 2023: Tue, 1:30–2:20 p.m.
Oct 11 – Dec 5, 2023: Tue, 1:30–2:20 p.m.
Sep 6 – Dec 5, 2023: Thu, 12:30–2:20 p.m.
Exam Times + Location:
Dec 11, 2023
Mon, 3:30–6:30 p.m.
1 778 782-4619
Prerequisites:CMPT 361 and MATH 152, both with a minimum grade of C-.
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.
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 and Matlab). 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 361 is highly recommended, which will become a prerequisite soon.
COURSE-LEVEL EDUCATIONAL GOALS:
- Image stitching
- Object detection
- Deep learning
Coding Assignments (100%)
MATERIALS + SUPPLIES:
- Computer Vision: Algorithms and Applications, Richard Szeliski,, Springer, 2011, 9781848829343, (Note it's downloadable as PDF from: http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf )
REQUIRED READING NOTES:
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