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Three-dimensional Gaussian Mixture Texture Model
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SYSNO ASEP 0467541 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Three-dimensional Gaussian Mixture Texture Model Author(s) Haindl, Michal (UTIA-B) RID, ORCID
Havlíček, Vojtěch (UTIA-B) RIDNumber of authors 2 Article number 1003 Source Title Proceedings of the 23rd International Conference on Pattern Recognition (ICPR). - Piscataway : IEEE, 2016 - ISBN 978-1-5090-4846-5 Pages s. 2026-2031 Number of pages 6 s. Publication form Print - P Action 23rd International Conference on Pattern Recognition ICPR 2016 Event date 04.12.2016 - 08.12.2016 VEvent location Cancún Country MX - Mexico Event type WRD Language eng - English Country US - United States Keywords bidirectional texture function ; Gaussian mixture model ; texture modeling Subject RIV BD - Theory of Information R&D Projects GA14-10911S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000406771302004 EID SCOPUS 85019076115 DOI 10.1109/ICPR.2016.7899934 Annotation Visual texture modeling based on multidimensional mathematical models is the prerequisite for both robust material recognition as well as for image restoration, compression or numerous physically correct virtual reality applications. A novel multispectral visual texture modeling method based on a descriptive, unusually complex, three-dimensional, spatial Gaussian mixture model is presented. Texture synthesis benefits from easy computation of arbitrary conditional distributions from the model. The model is inherently multispectral thus it does not suffer with the spectral quality compromises of the spectrally factorized alternative approaches. The model is especially well suited for multispectral textile textures and it can also describe the most advanced textural representation in the form of a bidirectional texture function (BTF). Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2017
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