0346556 20240103193758.720100913235959.9 000286412900041 77958510730 10.1007/978-3-642-14980-1_41 11 s. cav_un_epca*0346555978-3-642-14979-50302-9743423-433Berlin / HeidelbergSpringer Berlin / Heidelberg2010Hancock, Edwin and Wilson, Richard and Windeatt, Terry and Ulusoy, Ilkay and Escolano, Francisco texture degradation statistical features BTF psychophysics cav_un_auth*0101086 Filip Jiří 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. 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*0245455 Green P.R. GB http://library.utia.cas.cz/separaty/2010/RO/filip-a psychophysical evaluation of texture degradation descriptors.pdf 1M0572 GA MŠk cav_un_auth*0001814 ERG 239294 EC Marie Curie BE GA102/08/0593 GA ČR cav_un_auth*0239567 CEZ:AV0Z10750506 Delivering a digital realistic appearance of materials is one of the most difficult tasks of computer vision. Accurate representation of surface texture can be obtained by means of view and illumination dependent textures. However, this kind of appearance representation produces massive datasets so their compression is inevitable. For optimal visual performance of compression methods, their parameters should be set dependently on the actual material. We propose a set of statistical descriptors motivated by standard textural features, and psychophysically evaluate their performance on three subtle artificial texture visual degradations. We tested the five types of descriptors on five different textures and combination of thirteen surface shapes and two illuminations. We have found that descriptors based on two-dimensional causal auto-regressive model, have the highest correlation with the psychophysical results. cav_un_auth*0263645 Structural, Syntactic, and Statistical Pattern Recognition Cesme, Izmir 18.08.2010-20.08.2010 TR 2011 BD http://hdl.handle.net/11104/0187557 100 0.318 16.31 0.34 2010 77958510730 SCOPUS 000286412900041 WOS cav_un_epca*0346555 Structural, Syntactic, and Statistical Pattern Recognition 978-3-642-14979-5 0302-9743 423 433 Berlin / Heidelberg Springer Berlin / Heidelberg 2010 LNCS 6218 Hancock, Edwin and Wilson, Richard and Windeatt, Terry and Ulusoy, Ilkay and Escolano, Francisco 340