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Optimized Texture Spectral Similarity Criteria
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SYSNO ASEP 0546216 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Optimized Texture Spectral Similarity Criteria Author(s) Havlíček, Michal (UTIA-B) RID
Haindl, Michal (UTIA-B) RID, ORCIDNumber of authors 2 Article number 52 Source Title Advances in Computational Collective Intelligence. - Cham : Springer International Publishing, 2021 / Wojtkiewicz Krystian ; Treur Jan ; Pimenidis Elias ; Maleszka Marcin - ISSN 1865-0929 - ISBN 978-3-030-88113-9 Pages s. 644-655 Number of pages 12 s. Publication form Print - P Action International Conference on Computational Collective Intelligence 2021 /13./ Event date 29.09.2021 - 01.10.2021 VEvent location Kallithea, Rhodes Country GR - Greece Event type WRD Language eng - English Country CH - Switzerland Keywords Texture spectral similarity criterion ; Bidirectional Texture Function ; hyperspectral data ; texture modeling Subject RIV BD - Theory of Information OECD category Communication engineering and systems R&D Projects GA19-12340S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 DOI 10.1007/978-3-030-88113-9_52 Annotation This paper introduces an accelerated algorithm for evaluating criteria for comparing the spectral similarity of color, Bidirectional Texture Functions (BTF), and hyperspectral textures. The criteria credibly compare texture pixels by simultaneously considering the pixels with similar values and their mutual ratios. Such a comparison can determine the optimal modeling or acquisition setup by comparing the original data with their synthetic simulations. Other applications of the criteria can be spectral-based texture retrieval or classification. Together with existing alternatives, the suggested methods were extensively tested and compared on a wide variety of color, BTF, and hyper-spectral textures. The methods' performance quality was examined in a long series of specially designed 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 2022
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