0346560 20240103193759.020100907235959.9 10.1109/ICPR.2010.216 4 s. cav_un_epca*0346559978-1-4244-7542-11051-4651858-861Los AlamitosIEEE Computer Society CPS2010 texture colour Markov random field illumination invariance 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. 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/2010/RO/vacha-natural material recognition with illumination invariant textural features.pdf 1M0572 GA MŠk cav_un_auth*0001814 GA102/08/0593 GA ČR cav_un_auth*0239567 2C06019 GA MŠk CZ cav_un_auth*0216518 CEZ:AV0Z10750506 A visual appearance of natural materials fundamentally depends on illumination conditions, which significantly complicates a real scene analysis. We propose textural features based on fast Markovian statistics, which are simultaneously invariant to illumination colour and robust to illumination direction. No knowledge of illumination conditions is required and a recognition is possible from a single training image per material. Material recognition is tested on the currently most realistic visual representation - Bidirectional Texture Function (BTF), using the Amsterdam Library of Textures (ALOT), which contains 250 natural materials acquired in different illumination conditions. Our proposed features significantly outperform several leading alternatives including Local Binary Patterns (LBP, LBP-HF) and Gabor features. cav_un_auth*0263643 20th International Conference on Pattern Recognition ICPR 2010 Istanbul 23.08.2010-26.08.2010 TR 2011 BD http://hdl.handle.net/11104/0187560 50 0.420 10.52 0.85 2010 cav_un_epca*0346559 20th International Conference on Pattern Recognition 978-1-4244-7542-1 1051-4651 858 861 Los Alamitos IEEE Computer Society CPS 2010