0471593 20240103213656.520170224235959.9 85013468585 000418399200011 10.1007/978-3-319-52277-7_11 8 s. P cav_un_epca*0471591978-3-319-52276-084-92ChamSpringer International Publishing2017Beltran-CastanonC.NystromI.FamiliF. Textural features texture scale recognition sensitivity surface material recognition Markovian illumination invariant features 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. cav_un_auth*0213290 Vácha Pavel 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/2017/RO/haindl-0471593.pdf cav_un_auth*0303439 GA14-10911S GA ČR CZ Prevailing surface material recognition methods are based on textural features but most of these features are very sensitive to scale variations and the recognition accuracy significantly declines with scale incompatibility between visual material measurements used for learning and unknown materials to be recognized. This effect of mutual incompatibility between training and testing visual material measurements scale on the recognition accuracy is investigated for leading textural features and verified on a wood database, which contains veneers from sixty-six varied European and exotic wood species. The results show that the presented textural features, which are illumination invariants extracted from a generative multispectral Markovian texture representation, outperform the most common alternatives, such as Local Binary Patterns, Gabor features, or histogram-based approaches. cav_un_auth*0343392 CIARP 2016 - 21st Iberoamerican Congress 2016 20161108 20161111 Lima PE BD 10000 10200 10201 2018 2 PR RVO:67985556 http://hdl.handle.net/11104/0271350 S 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods|Imaging Science Photographic Technology 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods|Imaging Science Photographic Technology 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods|Imaging Science Photographic Technology 2017 85013468585 SCOPUS PUBMED 000418399200011 WOS cav_un_epca*0471591 Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 21st Iberoamerican Congress, CIARP 2016 Springer International Publishing 2017 Cham 84 92 978-3-319-52276-0 Lecture Notes in Computer Science 10125 340 Beltran-Castanon C. 340 Nystrom I. 340 Famili F.