Lehrinhalte
- Computer vision as (probabilistic) inference
- Robust estimation and modeling
- Foundations of Bayesian networks and Markov random fields
- Basic inference and learning methods in computer vision
- Image restoration
- Stereo
- Optical flow
- Bayesian tracking of (articulated) objects
- Semantic segmentation
- Current research topics

Literature
Literature recommendations will be updated regularly, an example might be:
- S. Prince, “Computer Vision: Models, Learning, and Inference”, Cambridge University Press, 2012
- R. Szeliski, ""Computer Vision: Algorithms and Applications"", Springer 2011

Voraussetzungen
Participation of lecture Visual Computing and Computer Vision I is recommended.

Semester: ST 2022