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BTF Compound Texture Model with Non-Parametric Control Field

  1. 1.
    SYSNO ASEP0492500
    Document TypeC - Proceedings Paper (int. conf.)
    R&D Document TypeConference Paper
    TitleBTF Compound Texture Model with Non-Parametric Control Field
    Author(s) Haindl, Michal (UTIA-B) RID, ORCID
    Havlíček, Vojtěch (UTIA-B) RID
    Number of authors2
    Source TitleThe 24th International Conference on Pattern Recognition (ICPR 2018). - New York : IEEE, 2018 - ISBN 978-1-5386-3787-6
    Pagess. 1151-1156
    Number of pages6 s.
    Publication formPrint - P
    ActionThe 24th International Conference on Pattern Recognition (ICPR 2018)
    Event date20.08.2018 - 24.08.2018
    VEvent locationBeijing
    CountryCN - China
    Event typeWRD
    Languageeng - English
    CountryUS - United States
    KeywordsCompound Markov random field model ; Bidirectional texture function ; Texture modeling
    Subject RIVBD - Theory of Information
    OECD categoryAutomation and control systems
    Institutional supportUTIA-B - RVO:67985556
    UT WOS000455146801028
    EID SCOPUS85059741571
    DOI10.1109/ICPR.2018.8545322
    AnnotationThis 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.
    WorkplaceInstitute of Information Theory and Automation
    ContactMarkéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
    Year of Publishing2019
Number of the records: 1  

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