Vorlesung: Markov Random Fields for Vision and Graphics
Veranstaltung
- Dozent(en):
- Beginn: 11.04.2018
- Zeiten: Wed. 12:30 (s.t.) - 14:00, SR U.027, Institut für Informatik
- Veranstaltungsnummer: MA-INF 2117
- Studiengang: Master
- Aufwand: 6 CP
- Prüfungen: Aug. 8, 2018
Übung
- Betreuer:
- Beginn: tbd
- Zeiten: Thu. 10:30 (s.t.) - 12:00, SR U.027, Institut für Informatik
Beschreibung
This course addresses advanced topics for Markov Random Fields and their use in applications for vision and graphics. We will cover advanced topics in inference and learning such as loopy belief propagation, MCMC sampling, graph cuts and move-making algorithms, dual decomposition and structured learning. Applications discussed will include low and mid-level vision and graphics concepts such as optical flow and stereo depth, super-resolution, superpixels, texture synthesis, segmentation as well as higher-level concepts such as semantic segmentation and object detection. It is recommended but not required to have taken Probabilistic Graphical Models (MA-INF 4315) as a pre-requisite for this course. Those who have not taken Probabilistic Graphical Models should be comfortable with concepts in probability theory and optimization.
News
First lecture starts on April 19, 2016!
Folien
- 01 Intro 120418 (PDF-Dokument, 16.4 MB)
- 02 BeliefProp 180418 (PDF-Dokument, 1.1 MB)
- 03 GraphCuts 250418 (PDF-Dokument, 3.4 MB)
- 04 SuperresTexture 020518 (PDF-Dokument, 26 MB)
- 05 ForegroundSeg 090518 (PDF-Dokument, 12.6 MB)
- 06 CRF (PDF-Dokument, 1.1 MB)
- 07 SemanticSegmentation 06062018 (PDF-Dokument, 19.9 MB)
- 08 Movemaking (PDF-Dokument, 5.6 MB)
- 09 OpticFlowDepth (PDF-Dokument, 3.7 MB)
- 10 DualDecomp 270618 (PDF-Dokument, 4.1 MB)
- 11 PartBasedModel 040718 (PDF-Dokument, 29 MB)
- 12 MaxMargin 110718 (PDF-Dokument, 63 KB)
Weitere Dokumente
- 03 StereoIntro (PDF-Dokument, 9.6 MB)
- 04 OpticFlowIntro (PDF-Dokument, 1.1 MB)
- 05 StructLearningShort (PDF-Dokument, 14.5 MB)
- MRF18 CourseInfo (PDF-Dokument, 45 KB)
Übungsaufgaben
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Übung 1: BeliefProp Übungsblatt (PDF-Dokument, 155 KB) |
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Übung 2: GraphCuts Übungsblatt (PDF-Dokument, 202 KB)
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Übung 3: SemanticSegmentation Übungsblatt (PDF-Dokument, 173 KB)
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Übung 4: PoseEstimation Übungsblatt (PDF-Dokument, 266 KB)
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