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Texture Spectral Similarity Criteria
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SYSNO ASEP 0508907 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Texture Spectral Similarity Criteria Author(s) Havlíček, Michal (UTIA-B) RID
Haindl, Michal (UTIA-B) RID, ORCIDNumber of authors 2 Source Title IET Image Processing. - : Wiley - ISSN 1751-9659
Roč. 13, č. 11 (2019), s. 1998-2007Number of pages 10 s. Publication form Print - P Language eng - English Country GB - United Kingdom Keywords Spectral similarity criterion ; bidirectional Texture Function ; hyper-spectral data ; texture modelling Subject RIV BD - Theory of Information OECD category Automation and control systems R&D Projects GA19-12340S GA ČR - Czech Science Foundation (CSF) Method of publishing Limited access Institutional support UTIA-B - RVO:67985556 UT WOS 000487789000023 EID SCOPUS 85072665108 DOI 10.1049/iet-ipr.2019.0250 Annotation New similarity criteria capable of assessing spectral modelling plausibility of colour, Bidirectional Texture Functions (BTF), and hyper-spectral textures are presented. The criteria credibly compare the multi-spectral pixel values of the textures. They simultaneously consider the pixels of similar values and their mutual ratios. It allows support of the optimal modelling or acquisition setup development by comparing the original data with its synthetic simulations. Analytical applications of the criteria can be spectral-based texture retrieval or classification. The suggested criteria together with existing alternatives are extensively tested and compared on a wide variety of colour, BTF, and hyper-spectral textures. The performance quality of the criteria is examined in a long series of thousands specially designed monotonically degrading experiments where proposed ones outperform all tested alternatives. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2020 Electronic address https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0250
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