0357314 20240103194941.320110304235959.9 000288922200002 79551605988 10.1016/j.patrec.2011.01.002 9 s. cav_un_epca*02573890167-8655326 (2011)771-779Elsevier Image modelling colour texture Illumination invariance Markov random field rotation 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. cav_un_auth*0101203 Suk Tomáš Zpracování obrazové informace Department of Image Processing ZOI ZOI UTIA-B Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2011/RO/vacha-0357314.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 significantly depends on acquisition circumstances, particularly illumination conditions and viewpoint position, whose variations cause difficulties in the analysis of real scenes. We address this issue with novel texture features, based on fast estimates of Markovian statistics, that are simultaneously rotation and illumination invariant. The proposed features are invariant to inplane material rotation and illumination spectrum (colour invariance), they are robust to local intensity changes (cast shadows) and illumination direction. No knowledge of illumination conditions is required and recognition is possible from a single training image per material. The material recognition is tested on the currently most realistic visual representation – Bidirectional Texture Function (BTF), using CUReT and ALOT texture datasets with more than 250 natural materials. 2011 BD 4 A 4a 20231122134454.9 http://hdl.handle.net/11104/0195620 COMPUTERSCIENCEARTIFICIALINTELLIGENCE 1.724 0.084 6.9 0.01507 0.712 4746 262 82 0.662 26.64 2.46 Q2 67.28 43.694 Q2 Q2 2011 Soubory v repozitáři: vacha-0357314.pdf 79551605988 SCOPUS 000288922200002 WOS cav_un_epca*0257389 Pattern Recognition Letters 0167-8655 1872-7344 Roč. 32 č. 6 2011 771 779 Elsevier