Optimized Texture Spectral Similarity Criteria

0546216 - ÚTIA 2022 RIV CH eng C - Conference Paper (international conference)
Havlíček, Michal - Haindl, Michal
Optimized Texture Spectral Similarity Criteria.
Advances in Computational Collective Intelligence. Cham: Springer International Publishing, 2021 - (Wojtkiewicz, K.; Treur, J.; Pimenidis, E.; Maleszka, M.), s. 644-655, č. článku 52. Communications in Computer and Information Science, 1463. ISBN 978-3-030-88113-9. ISSN 1865-0929.
[International Conference on Computational Collective Intelligence 2021 /13./. Kallithea, Rhodes (GR), 29.09.2021-01.10.2021]
R&D Projects: GA ČR(CZ) GA19-12340S
Institutional support: RVO:67985556
Keywords : Texture spectral similarity criterion * Bidirectional Texture Function * hyperspectral data * texture modeling
OECD category: Communication engineering and systems
http://library.utia.cas.cz/separaty/2021/RO/haindl-0546216.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0323757