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BTF Compound Texture Model with Non-Parametric Control Field
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SYSNO ASEP 0492500 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title BTF Compound Texture Model with Non-Parametric Control Field Author(s) Haindl, Michal (UTIA-B) RID, ORCID
Havlíček, Vojtěch (UTIA-B) RIDNumber of authors 2 Source Title The 24th International Conference on Pattern Recognition (ICPR 2018). - New York : IEEE, 2018 - ISBN 978-1-5386-3787-6 Pages s. 1151-1156 Number of pages 6 s. Publication form Print - P Action The 24th International Conference on Pattern Recognition (ICPR 2018) Event date 20.08.2018 - 24.08.2018 VEvent location Beijing Country CN - China Event type WRD Language eng - English Country US - United States Keywords Compound Markov random field model ; Bidirectional texture function ; Texture modeling Subject RIV BD - Theory of Information OECD category Automation and control systems Institutional support UTIA-B - RVO:67985556 UT WOS 000455146801028 EID SCOPUS 85059741571 DOI 10.1109/ICPR.2018.8545322 Annotation This paper introduces a novel multidimensional statistical model for realistic modeling, enlargement, editing, and compression of the recent state-of-the-art bidirectional texture function (BTF) textural representation. The presented multispectral compound Markov random field model (CMRF) efficiently fuses a non-parametric random field model with several parametric random fields models. The primary purpose of our modeling texture approach is to reproduce, compress, and enlarge a given measured natural or artificial texture image so that ideally both natural and synthetic texture will be visually indiscernible for any observation or illumination directions. However, the model can be easily applied for BFT material texture editing as well. The CMRF model consists of several parametric sub-models each having different characteristics along with an underlying switching structure model which controls transitions between these submodels. The proposed model uses the non-parametric random field for distributing local texture models in the form of analytically solvable wide-sense BTF Markov representation for single regions among the fields of a mosaic approximated by the random field structure model. The non-parametric control field of BTF-CMRF is reiteratively generated to guarantee identical region-size histograms for all material sub-classes present in the target example texture. The local texture regions (not necessarily continuous) are represented by analytical BTF models modeled by the adaptive 3D causal auto-regressive (3DCAR) random field model which can be analytically estimated as well as synthesized. The visual quality of the resulting complex synthetic textures generally surpasses the outputs of the previously published simpler non-compound BTF-MRF models. The model allows reaching huge compression ratio incomparable with any standard image compression method.
Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2019
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