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teaching:ss2019:pgm2019 [2019/07/10 03:31] wuta |
teaching:ss2019:pgm2019 [2019/07/13 01:48] wuta |
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- | Several problems in computer vision can be cast as a labeling problem. Typically, such problems arise from Markov Random Field (MRF) models, which provide an elegant framework of formulating various types of labeling problems in imaging. | + | Several problems in computer vision can be cast as a labeling problem. Typically, such problems arise from Markov Random Field (undirected graphical models), which provide an elegant framework of formulating various types of labeling problems in vision. |
- | By making use of certain assumptions some „nice“ MRF models can be solved in polynomial time, whereas | + | Under certain assumptions some "nice" |
{{ : | {{ : | ||
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- Approximative inference techniques: | - Approximative inference techniques: | ||
* Loopy belief propagation | * Loopy belief propagation | ||
- | * Mean field, variational inference | + | * Mean field, |
* Sampling methods | * Sampling methods | ||
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Date: August 05th, 08:30 - 09:45. \\ | Date: August 05th, 08:30 - 09:45. \\ | ||
Place: 102, Interims Hörsaal 2 (5620.01.102) \\ | Place: 102, Interims Hörsaal 2 (5620.01.102) \\ | ||
- | The final exam will be written. | + | The final exam will be written. No cheat sheet is allowed. |
== Lecture Materials == | == Lecture Materials == | ||
Course material (slides and exercise sheets) can be accessed [[teaching: | Course material (slides and exercise sheets) can be accessed [[teaching: |