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]
Literatur
[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) ist 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 image analysis tasks on realistic imagery.
Zusätzliche Informationen
[url]http://www.visinf.tu-darmstadt.de/vi_teaching/vi_lectures/index.en.jsp[/url]
Bemerkung Webportal
[b]Area:[/b] Human Computer Systems<br /> <br />
Online-Angebote
moodle
[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]
Literatur
[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) ist 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 image analysis tasks on realistic imagery.
Zusätzliche Informationen
[url]http://www.visinf.tu-darmstadt.de/vi_teaching/vi_lectures/index.en.jsp[/url]
Bemerkung Webportal
[b]Area:[/b] Human Computer Systems<br /> <br />
Online-Angebote
moodle
- Lehrende: Stefan Roth
Semester: Inverno 2020/21