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3D Multi-frequency Fully Correlated Causal Random Field Texture Model
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SYSNO ASEP 0522438 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title 3D Multi-frequency Fully Correlated Causal Random Field Texture Model Author(s) Haindl, Michal (UTIA-B) RID, ORCID
Havlíček, Vojtěch (UTIA-B) RIDNumber of authors 2 Article number 33 Source Title Pattern Recognition. - Cham : Springer International Publishing, 2020 / Palaiahnakote Shivakumara ; Sanniti di Baja Gabriella ; Wang Liang ; Yan Wei Qi - ISSN 0302-9743 - ISBN 978-3-030-41298-2 Pages s. 423-434 Number of pages 12 s. Publication form Print - P Action The 5th Asian Conference on Pattern Recognition (ACPR 2019) Event date 26.11.2019 - 29.11.2019 VEvent location Auckland Country NZ - New Zealand Event type WRD Language eng - English Country CH - Switzerland Keywords texture modeling ; Markov random field ; Bidirectional Texture Function Subject RIV BD - Theory of Information OECD category Applied mathematics R&D Projects GA19-12340S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 EID SCOPUS 85081552252 DOI 10.1007/978-3-030-41299-9_33 Annotation We propose a fast novel multispectral texture model with an analytical solution for both parameter estimation as well as unlimited synthesis. This Gaussian random field type of model combines a principal random field containing measured multispectral pixels with an auxiliary random field resulting from a given function whose argument is the principal field data.
The model can serve as a stand-alone texture model or a local model for more complex compound random field or bidirectional texture function models.
The model can be beneficial not only for texture synthesis, enlargement, editing, or compression but also for high accuracy texture recognition.Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2021
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