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Instructors: Prof. Ph. D. Stefan Roth
Event type:
Integrated Course
Org-unit: Dept. 20 - Computer Science
Displayed in timetable as:
CV II
Subject:
Crediting for:
Hours per week:
4
Language of instruction:
Englisch
Min. | Max. participants:
- | -
Course Contents:
- 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
Preconditions:
Participation of lecture Visual Computing and Computer Vision I is recommended.
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