0452917 20240111140913.620160215235959.9 84939611663 000392313200003 10.1007/s00371-015-1148-1 11 s. P cav_un_epca*02553350178-2789331 (2017)5-15Springer anisotropic Highlight stencils BRDF model cav_un_auth*0101086 Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition 60 Filip Jiří UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0283206 Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition 20 Havlíček Michal UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0282273 Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition 20 Vávra Radomír UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2016/RO/filip-0452917.pdf 4MB cav_un_auth*0303412 GA14-02652S GA ČR CZ Directionally dependent anisotropic material appearance phenomenon is widely represented using bidirectional reflectance distribution function (BRDF). This function needs in practice either reconstruction of unknown values interpolating between sparse measured samples or requires data fidelity preserving compression forming a compact representation from dense measurements. Both properties can be, to a certain extent, preserved by means of analytical BRDF models. Unfortunately, the number of anisotropic BRDF models is limited, and moreover, most require either a demanding iterative optimization procedure dependent on proper initialization or the user setting parameters. Most of these approaches are challenged by the fitting of complex anisotropic BRDFs. In contrast, we approximate BRDF anisotropic behavior by means of highlight stencils and derive a novel BRDF model that independently adapts such stencils to each anisotropic mode present in the BRDF. Our model allows for the fast direct fitting of parameters without the need of any demanding optimization. BD 10000 10100 10102 2017 4 A hod 4ah 20231122141407.2 RVO:67985556 http://hdl.handle.net/11104/0257066 1 Department of Pattern Recognition UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE S RIV/67985556:_____/17:00452917!RIV17-AV0-67985556 191870804 Doplnění OECD UTIA-B RIV/67985556:_____/17:00452917!RIV17-GA0-67985556 191876835 Doplnění OECD UTIA-B 3+4 Article Computer Science Software Engineering 3+4 Article Computer Science Software Engineering 3+4 Article Computer Science Software Engineering COMPUTERSCIENCE.SOFTWAREENGINEERING 1.398 0.358 6.7 0.00383 0.489 1829 0.401 0.821 120 Q3 49.229 36.1 Q3 Q3 0.74 Q3 36.058 2017 Soubory v repozitáři: filip-0452917.pdf 84939611663 SCOPUS 000392313200003 WOS 4MB cav_un_epca*0255335 Visual Computer 0178-2789 1432-2315 Roč. 33 č. 1 2017 5 15 Springer