0492498 20240103220353.920180827235959.9 85052215669 10.1007/978-3-319-97785-0_3 10 s. P cav_un_epca*0492497978-3-319-97784-30302-974322-31ChamSpringer Nature Switzerland AG2018BaiX.HancockE.HoT.WilsonR.BiggioB.Robles-KellyA. Bark recognition Tree taxonomy clasification Spiral Markov random field model cav_un_auth*0286710 Remeš Václav Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101093 Haindl Michal Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2018/RO/haindl-0492498.pdf An efficient bark recognition method based on a novel wide-sense Markov spiral model textural representation is presented. 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 method significantly outperforms the state-of-the-art bark recognition approaches in terms of the classification accuracy. cav_un_auth*0363300 IAPR Joint International Workshop on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recognition 20180817 20180819 Beijing CN BD 20000 20200 20205 2019 2 PO RVO:67985556 http://hdl.handle.net/11104/0286554 S 3 0.339 Q2 Q2 2018 85052215669 SCOPUS PUBMED WOS cav_un_epca*0492497 Structural, Syntactic, and Statistical Pattern Recognition : Joint IAPR International Workshop, S+SSPR 2018 978-3-319-97784-3 0302-9743 22 31 Cham Springer Nature Switzerland AG 2018 Lecture Notes in Computer Science 11004 340 Bai X. 340 Hancock E. 340 Ho T. 340 Wilson R. 340 Biggio B. 340 Robles-Kelly A.