0380289 20240103201154.420120921235959.9 10.1007/978-3-642-32436-9_13 11 s. P cav_un_epca*0380282978-3-642-32435-20302-9743149-159BerlinSpringer2012 bidirectional texture function texture analysis texture synthesis data compression virtual reality 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*0283206 Havlíček 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/haindl-bidirectional texture function simultaneous autoregressive model.pdf 1M0572 GA MŠk cav_un_auth*0001814 387/2010 CESNET CZ GA102/08/0593 GA ČR cav_un_auth*0239567 GAP103/11/0335 GA ČR cav_un_auth*0273627 The Bidirectional Texture Function (BTF) is the recent most advanced representation of visual properties of surface materials. It specifies their altering appearance due to varying illumination and viewing conditions. Corresponding huge BTF measurements require a mathematical representation allowing simultaneously extremal compression as well as high visual fidelity. We present a novel Markovian BTF model based on a set of underlying simultaneous autoregressive models (SAR). This complex but efficient BTF-SAR model combines several multispectral band limited spatial factors and range map sub-models to produce the required BTF texture space. The BTF-SAR model enables very high BTF space compression ratio, texture enlargement, and reconstruction of missing unmeasured parts of the BTF space. cav_un_auth*0283201 MUSCLE Pisa 13.12.2011-15.12.2011 IT 2013 BD 2 PR RVO:67985556 http://hdl.handle.net/11104/0211031 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 149 159 Berlin Springer 2012 Lecture Notes in Computer Science 7252