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Texture recognition under scale and illumination variations
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SYSNO ASEP 0584124 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Texture recognition under scale and illumination variations Author(s) Vácha, Pavel (UTIA-B) RID
Haindl, Michal (UTIA-B) RID, ORCIDNumber of authors 2 Source Title Journal of Information and Telecommunication - ISSN 2475-1839
Roč. 8, č. 1 (2024), s. 130-148Number of pages 19 s. Publication form Print - P Language eng - English Country GB - United Kingdom Keywords Markovian Textural features ; LBP ; Gabor features ; scale sensitivity ; illumination sensitivity 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 Open access Institutional support UTIA-B - RVO:67985556 UT WOS 001080214500001 EID SCOPUS 85173756924 DOI 10.1080/24751839.2023.2265190 Annotation Visual scene recognition is predominantly based on visual textures representing an object's material properties. However, the single material texture varies in scale and illumination angles due to mapping an object's shape. We present a comparative study of the color histogram, Gabor, opponent Gabor, Local Binary Pattern (LBP), and wide-sense Markovian textural features concerning their sensitivity to simultaneous scale and illumination variations. Due to their application dominance, these textural features are selected from more than 50 published textural features.
Markovian features are information preserving, and we demonstrate their superior performance for scale and illumination variable observation conditions over the standard alternative textural features. We bound the scale variation by double size, and illumination variation includes illumination spectra, acquisition devices, and 35 illumination directions spanned above a sample hemisphere. Recognition accuracy is tested on textile patterns from the University of East Anglia and wood veneers from UTIA BTF databases.Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2025 Electronic address https://www.tandfonline.com/doi/full/10.1080/24751839.2023.2265190
Number of the records: 1