Graphical Model Inference and Learning for Visual Computing

Abstract : Computational vision and image analysis is a multidisciplinary scientific field that aims to make computers "see" in a way that is comparable to human perception. It is currently one of the most challenging research areas in artificial intelligence. In this regard, the extraction of information from the vast amount of visual data that are available today as well as the exploitation of the resulting information space becomes one of the greatest challenges in our days. To address such a challenge, this thesis describes a very general computational framework that can be used for performing efficient inference and learning for visual perception based on very rich and powerful models.
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Computer Vision and Pattern Recognition [cs.CV]. Université Paris-Est, 2013
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https://tel.archives-ouvertes.fr/tel-00866078
Contributeur : Nikos Komodakis <>
Soumis le : mercredi 25 septembre 2013 - 19:24:16
Dernière modification le : samedi 11 février 2017 - 01:06:37
Document(s) archivé(s) le : jeudi 26 décembre 2013 - 04:25:25

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  • HAL Id : tel-00866078, version 1

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Nikos Komodakis. Graphical Model Inference and Learning for Visual Computing. Computer Vision and Pattern Recognition [cs.CV]. Université Paris-Est, 2013. <tel-00866078>

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