Digital Teaching
Digital course materials will be made available through Moodle.
Course Contents
[list]
[*]Basics of image formation
[*]Linear and (simple) nonlinear image filtering
[*]Foundations of multi-view geometry
[*]Camera calibration and pose estimation
[*]Foundations of 3D reconstruction
[*]Foundations of motion estimation from video
[*]Basic approaches to object recognition
[*]Object classification
[*]Object detection
[*]Deep networks in computer vision
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Literature
[list]
[*]R. Szeliski, "Computer Vision: Algorithms and Applications", 2nd edition, Springer 2022
[*]D. Forsyth, J. Ponce, "Computer Vision - A Modern Approach", 2nd edition, Prentice Hall, 2015
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Preconditions
Having previously attended Visual Computing (20-00-0014-iv, formerly: Kanonik Human Computer Systems) is recommended.
Further Grading Information
After successfully attending the course, students are familiar with the basics of computer vision. They understand fundamental techniques for the analysis of images and videos, can name their assumptions and mathematical formulations, as well as describe the resulting algorithms. They are able to implement these techniques in order to solve basic computer vision tasks on realistic imagery.
Additional Information
The course will be offered in parallel in an in-presence and in a digital format. An in-person participation is strongly encouraged, however, digital participation is possible without any restrictions. The digital course material is equivalent to the in-presence material and will be made available through Moodle (potentially with a delay).
Online Offerings
moodle
- Lehrende: Stefan Roth