0380288 20240103201154.320120921235959.9 10.1007/978-3-642-32436-9_11 12 s. P cav_un_epca*0380282978-3-642-32435-20302-9743126-137BerlinSpringer2012 texture recognition illumination invariance Markov random fields Bidirectional Texture Function textural databases 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/2012/RO/vacha-texture recognition using robust markovian features.pdf 1M0572 GA MŠk cav_un_auth*0001814 387/2010 CESNET CZ GAP103/11/0335 GA ČR cav_un_auth*0273627 GA102/08/0593 GA ČR cav_un_auth*0239567 We provide a thorough experimental evaluation of several state-of-the-art textural features on four representative and extensive image data/-bases. Each of the experimental textural databases ALOT, Bonn BTF, UEA Uncalibrated, and KTH-TIPS2 aims at specific part of realistic acquisition conditions of surface materials represented as multispectral textures. The extensive experimental evaluation proves the outstanding reliable and robust performance of efficient Markovian textural features analytically derived from a wide-sense Markov random field causal model. These features systematically outperform leading Gabor, Opponent Gabor, LBP, and LBP-HF alternatives. Moreover, they even allow successful recognition of arbitrary illuminated samples using a single training image per material. Our features are successfully applied also for the recent most advanced textural representation in the form of 7-dimensional Bidirectional Texture Function (BTF). cav_un_auth*0283201 MUSCLE Pisa 13.12.2011-15.12.2011 IT 2013 BD 2 PR RVO:67985556 http://hdl.handle.net/11104/0211030 100 0.314 16.66 0.49 Q2 Q2 2012 cav_un_epca*0380282 Computational Intelligence for Multimedia Understanding 978-3-642-32435-2 0302-9743 126 137 Berlin Springer 2012 Lecture Notes in Computer Science 7252