Digital Teaching
The digital course materials will be made available through Moodle.
Lehrinhalte
[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
[*]Template and subspace methods for object recognition
[*]Object classification
[*]Object detection
[*]Deep networks in computer vision
[/list]
Literature
[list]
[*]R. Szeliski, "Computer Vision: Algorithms and Applications", Springer 2011
[*]D. Forsyth, J. Ponce, "Computer Vision - A Modern Approach", Prentice Hall, 2002
[/list]
Voraussetzungen
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.
Zusätzliche Informationen
The course will be offered in parallel in an in-presence and in a digital format. Students who wish to participate physically (i.e. in the lecture hall) need to register ahead of time through the course Moodle. 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.
Bemerkung Webportal
[b]Area:[/b] Human Computer Systems<br /> <br />
Online-Angebote
moodle
The digital course materials will be made available through Moodle.
Lehrinhalte
[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
[*]Template and subspace methods for object recognition
[*]Object classification
[*]Object detection
[*]Deep networks in computer vision
[/list]
Literature
[list]
[*]R. Szeliski, "Computer Vision: Algorithms and Applications", Springer 2011
[*]D. Forsyth, J. Ponce, "Computer Vision - A Modern Approach", Prentice Hall, 2002
[/list]
Voraussetzungen
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.
Zusätzliche Informationen
The course will be offered in parallel in an in-presence and in a digital format. Students who wish to participate physically (i.e. in the lecture hall) need to register ahead of time through the course Moodle. 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.
Bemerkung Webportal
[b]Area:[/b] Human Computer Systems<br /> <br />
Online-Angebote
moodle
- Lehrende: Stefan Roth
Semester: WT 2021/22