0497357 20240103221021.720181130235959.9 85065912246 000469258400014 10.1109/SITIS.2018.00025 8 s. E cav_un_epca*0497818978-1-5386-9385-898-105Los AlamitosIEEE Computer Society CPS2018Sanniti di BajaG.GalloL.YetongnonK.DipandaA.Castrillón-SantanaM.ChbeirR. BTF texture model compound Markov random field BTF texture synthesis cav_un_auth*0101093 Haindl Michal UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101100 Havlíček Vojtěch UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2018/RO/haindl-0497357.pdf We propose a substantial speed up a modification to our recently published 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 multispectral compound Markov random field model (CMRF) efficiently fuses a non-parametric random field model with several parametric Markovian random fields models. The principal application of our model is physically correct and realistic synthetic imitation of material texture, its enlargement, and huge compression. So that ideally, both natural and synthetic texture of a given measured natural or artificial texture will be visually indiscernible for any observation or illumination directions. The presented model can be easily applied also for BTF material texture editing to model non-measured or unmeasurable but still realistic material textures. 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 the BTF-CMRF is iteratively generated to guarantee identical region-size histograms for all material sub-classes present in the target example texture. The present iterative algorithm significantly cuts the number of iterations to converge in comparison with our previous iterative method and even sometimes skip all iteration due to its ingenious initialization. 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 and allows to reach tremendous compression ratio incomparable with any standard image compression method. cav_un_auth*0368592 SITIS 2018. International Conference on Signal Image Technology & Internet Based Systems /14./ 20181126 20181129 Las Palmas de Gran Canaria ES BD 20000 20200 20202 2019 2 PR RVO:67985556 http://hdl.handle.net/11104/0290643 S 25 RIV/67985556:_____/18:00497357!RIV19-AV0-67985556 192095229 Doplnění UT WOS a SCOPUS 3+4 Proceedings Paper Computer Science Software Engineering|Computer Science Theory Methods|Engineering Electrical Electronic 2018 85065912246 SCOPUS PUBMED 000469258400014 WOS cav_un_epca*0497818 SITIS 2018. Proceedings of the 14th International Conference on Signal-Image Technology & Internet-Based Systems IEEE Computer Society CPS 2018 Los Alamitos 98 105 978-1-5386-9385-8 340 Sanniti di Baja G. 340 Gallo L. 340 Yetongnon K. 340 Dipanda A. 340 Castrillón-Santana M. 340 Chbeir R.