0492500 20240103220354.020180827235959.9 85059741571 000455146801028 10.1109/ICPR.2018.8545322 6 s. P cav_un_epca*0492502978-1-5386-3787-61151-1156New YorkIEEE2018 Compound Markov random field model Bidirectional texture function 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/2018/RO/haindl-0492500.pdf This paper introduces a novel multidimensional statistical model for realistic modeling, enlargement, editing, and compression of the recent state-of-the-art bidirectional texture function (BTF) textural representation. The presented multispectral compound Markov random field model (CMRF) efficiently fuses a non-parametric random field model with several parametric random fields models. The primary purpose of our modeling texture approach is to reproduce, compress, and enlarge a given measured natural or artificial texture image so that ideally both natural and synthetic texture will be visually indiscernible for any observation or illumination directions. However, the model can be easily applied for BFT material texture editing as well. The CMRF model consists of several parametric sub-models each having different characteristics along with an underlying switching structure model which controls transitions between these submodels. The proposed model uses the non-parametric random field for distributing local texture models in the form of analytically solvable wide-sense BTF Markov representation for single regions among the fields of a mosaic approximated by the random field structure model. The non-parametric control field of BTF-CMRF is reiteratively generated to guarantee identical region-size histograms for all material sub-classes present in the target example texture. The local texture regions (not necessarily continuous) are represented by analytical BTF models modeled by the adaptive 3D causal auto-regressive (3DCAR) random field model which can be analytically estimated as well as synthesized. The visual quality of the resulting complex synthetic textures generally surpasses the outputs of the previously published simpler non-compound BTF-MRF models. The model allows reaching huge compression ratio incomparable with any standard image compression method. cav_un_auth*0363310 The 24th International Conference on Pattern Recognition (ICPR 2018) 20180820 20180824 Beijing CN BD 20000 20200 20205 2019 2 4 A hod 4ah 20231122143343.1 PR RVO:67985556 http://hdl.handle.net/11104/0286556 1 Department of Pattern Recognition UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE S n.a. Proceedings Paper Computer Science Artificial Intelligence 2018 Soubory v repozitáři: haindl-0492500.pdf 85059741571 SCOPUS PUBMED 000455146801028 WOS cav_un_epca*0492502 The 24th International Conference on Pattern Recognition (ICPR 2018) 978-1-5386-3787-6 1151 1156 New York IEEE 2018