0471592 20240103213656.420170224235959.9 85013427588 000418399200006 10.1007/978-3-319-52277-7_6 8 s. P cav_un_epca*0471591978-3-319-52276-044-51ChamSpringer International Publishing2017Beltran-CastanonC.NystromI.FamiliF. Texture texture synthesis compound random field model CAR model two-dimensional Bernoulli mixture two-dimensional Gaussian mixture bidirectional texture function cav_un_auth*0101093 Haindl Michal Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition A 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/2017/RO/haindl-0471592.pdf cav_un_auth*0303439 GA14-10911S GA ČR CZ Two novel models for texture representation using parametric compound random field models are introduced. These models consist of a set of several sub-models each having different characteristics along with an underlying structure model which controls transitions between them. The structure model is a two-dimensional probabilistic mixture model either of the Bernoulli or Gaussian mixture type. Local textures are modeled using the fully multispectral three-dimensional causal auto-regressive models. Both presented compound random field models allow to reproduce, compress, edit, and enlarge a given measured color, multispectral, or bidirectional texture function (BTF) texture so that ideally both measured and synthetic textures are visually indiscernible. cav_un_auth*0343392 CIARP 2016 - 21st Iberoamerican Congress 2016 20161108 20161111 Lima PE BD 10000 10200 10201 2018 2 PR RVO:67985556 http://hdl.handle.net/11104/0271351 S n.a. Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods|Imaging Science Photographic Technology n.a. Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods|Imaging Science Photographic Technology n.a. Proceedings Paper Computer Science Artificial Intelligence|Computer Science Theory Methods|Imaging Science Photographic Technology 2017 85013427588 SCOPUS PUBMED 000418399200006 WOS cav_un_epca*0471591 Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 21st Iberoamerican Congress, CIARP 2016 Springer International Publishing 2017 Cham 44 51 978-3-319-52276-0 Lecture Notes in Computer Science 10125 340 Beltran-Castanon C. 340 Nystrom I. 340 Famili F.