0506602 20240103222303.520190719235959.9 85068558335 000482374500084 10.1016/j.patrec.2019.06.027 6 s. P cav_un_epca*02573890167-86551251 (2019)612-617Elsevier Bark recognition Tree taxonomy clasification Spiral Markov random field model textural feature cav_un_auth*0286710 Remeš Václav 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*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. http://library.utia.cas.cz/separaty/2019/RO/haindl-0506602.pdf https://www.sciencedirect.com/science/article/pii/S0167865519301886 cav_un_auth*0376011 GA19-12340S GA ČR CZ We present novel rotationally invariant fully multispectral Markovian textural features applied for the efficient tree bark recognition. These textural features are derived from the novel descriptive multispectral spiral wide-sense Markov model. Unlike the alternative bark recognition methods based on various gray-scale discriminative textural descriptions, we benefit from fully descriptive color, rotationally invariant bark texture representation. The proposed methods significantly outperform the state-of-the-art bark recognition approaches regarding classification accuracy. Both our classifiers outperform convolutional neural network ResNet even on the largest public bark database BarkNet which contains 23 000 high-resolution images from 23 different tree species. WOS BD 20000 20200 20202 2020 2 4 A hod 4ah 20231122144131.0 RVO:67985556 http://hdl.handle.net/11104/0297826 1 Department of Pattern Recognition UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE S 3+4 Article Multidisciplinary Sciences C COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE 3.077 0.921 9.3 0.01471 0.844 13401 188 0.848 34.77 4.21 3660 Q1 2.996 369 Q2 62.394 68.2 Q2 Q4 0.74 Q2 68.248 2019 Soubory v repozitáři: haindl-0506602.pdf 85068558335 SCOPUS PUBMED 000482374500084 WOS cav_un_epca*0257389 Pattern Recognition Letters 0167-8655 1872-7344 Roč. 125 č. 1 2019 612 617 Elsevier