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Rich probabilistic models for semantic labeling

descriptionPublicationkeyboard_double_arrow_right Doctoral thesis , Thesis 01 Jan 2016Embargo end date: 13 Dec 2019 English
Authors: Yang, Michael Ying;

doi: 10.15488/8910

Rich probabilistic models for semantic labeling

Abstract

Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere Beiträge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinären Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung.

Keywords

remote sensing, Dewey Decimal Classification::000 | Allgemeines, Wissenschaft::000 | Informatik, Wissen, Systeme::004 | Informatik, Computer vision, Bildverarbeitung, image processing

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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