0510488 20240103222838.120191106235959.9 85076168125 000582481300012 10.1007/978-3-030-33720-9_12 12 s. P cav_un_epca*0510482978-3-030-33719-30302-9743156-167ChamSpringer2019BebisG.BoyleR.ParvinB.KoracinD. convolutional neural network texture recognition Bidirectional Texture Function recognition cav_un_auth*0101165 Mikeš Stanislav 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 K Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2019/RO/haindl-0510488.pdf GA19-12340S GA ČR CZ cav_un_auth*0376011 The paper presents a detailed study of surface material recognition dependence on the illumination and viewing conditions which is a hard challenge in a realistic scene interpretation. The results document sharp classification accuracy decrease when using usual texture recognition approach, i.e., small learning set size and the vertical viewing and illumination angle which is a very inadequate representation of the enormous material appearance variability. The visual appearance of materials is considered in the state-of-the-art Bidirectional Texture Function (BTF) representation and measured using the upper-end BTF gonioreflectometer. The materials in this study are sixty-five different wood species. The supervised material recognition uses the shallow convolutional neural network (CNN) for the error analysis of angular dependency. We propose a Gaussian mixture model-based method for robust material segmentation. cav_un_auth*0382047 International Symposium on Visual Computing (ISVC 2019) /14./ 20191007 20191009 Lake Tahoe US BD 20000 20200 20205 2020 2 4 A sml 4as 20231122144408.5 PR RVO:67985556 http://hdl.handle.net/11104/0302678 S Contract Book Contributor Consent to Publish 20190904 12 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Software Engineering 2019 Soubory v repozitáři: haindl-0510488-Contract_Book_Contributor_Consent_to_Publish_LNCS_SIP_MH.pdf 85076168125 SCOPUS PUBMED 000582481300012 WOS cav_un_epca*0510482 Advances in Visual Computing : 14th International Symposium on Visual Computing (ISVC 2019) 978-3-030-33719-3 0302-9743 1611-3349 156 167 Cham Springer 2019 Lecture Notes in Computer Science 11844 340 Bebis G. 340 Boyle R. 340 Parvin B. 340 Koracin D.