0520496 20241106135819.820200120235959.9 85078695401 10.1109/IVCNZ48456.2019.8961023 6 s. P cav_un_epca*0520495978-1-7281-4188-62151-21911-6PiscatawayIEEE2019 Coniferous needles categorization Tree taxonomy recognition Spiral Markov random field model cav_un_auth*0101093 Haindl Michal UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101239 Žid Pavel UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2020/RO/haindl-0520496.pdf cav_un_auth*0376011 GA19-12340S GA ČR CZ This paper introduces multispectral rotationally invariant textural features of the Markovian type applied for the effective coniferous tree needles categorization. Presented texture features are inferred from the descriptive multispectral spiral wide-sense Markov model. Unlike the alternative texture recognition methods based on various gray-scale discriminative textural descriptions, we take advantage of the needles texture representation, which is fully descriptive multispectral and rotationally invariant. The presented method achieves high accuracy for needles recognition. Thus it can be used for reliable coniferous tree taxon classification. Our classifier is tested on the open source needles database Aff, which contains 716 high-resolution images from 11 diverse coniferous tree species. cav_un_auth*0387868 Image and Vision Computing New Zealand (IVCNZ 2019) /34./ 20191202 20191204 Dunedin NZ BD 20000 20200 20205 2020 2 4 A sml 4as 20241106135819.8 PR RVO:67985556 http://hdl.handle.net/11104/0305414 1 S Copyright receipt 20191013 0.125 Q4 2019 Soubory v repozitáři: haindl-0520496-CopyrightReceipt (1).pdf 85078695401 SCOPUS PUBMED WOS cav_un_epca*0520495 International Conference on Image and Vision Computing New Zealand 2019 (IVCNZ 2019) 978-1-7281-4188-6 2151-2191 1 6 Piscataway IEEE 2019