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.
- 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.
- Lehrende: RothStefan
Semester: ST 2022