0467541 20240103213205.220161219235959.9 85019076115 000406771302004 10.1109/ICPR.2016.7899934 6 s. P cav_un_epca*0467540978-1-5090-4846-52026-2031PiscatawayIEEE2016 bidirectional texture function Gaussian mixture model texture modeling 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*0101100 Havlíček Vojtěch 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/2016/RO/haindl-0467541.pdf cav_un_auth*0303439 GA14-10911S GA ČR CZ Visual texture modeling based on multidimensional mathematical models is the prerequisite for both robust material recognition as well as for image restoration, compression or numerous physically correct virtual reality applications. A novel multispectral visual texture modeling method based on a descriptive, unusually complex, three-dimensional, spatial Gaussian mixture model is presented. Texture synthesis benefits from easy computation of arbitrary conditional distributions from the model. The model is inherently multispectral thus it does not suffer with the spectral quality compromises of the spectrally factorized alternative approaches. The model is especially well suited for multispectral textile textures and it can also describe the most advanced textural representation in the form of a bidirectional texture function (BTF). cav_un_auth*0340495 23rd International Conference on Pattern Recognition ICPR 2016 20161204 20161208 Cancún MX BD 2017 2 PO RVO:67985556 http://hdl.handle.net/11104/0266451 S 1003 3+4 Proceedings Paper Computer Science Artificial Intelligence 2016 85019076115 SCOPUS PUBMED 000406771302004 WOS cav_un_epca*0467540 Proceedings of the 23rd International Conference on Pattern Recognition (ICPR) 978-1-5090-4846-5 2026 2031 Piscataway IEEE 2016