0471592 - ÚTIA 2018 RIV DE eng C - Conference Paper (international conference)
Haindl, Michal - Havlíček, Vojtěch
Two Compound Random Field Texture Models.
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 21st Iberoamerican Congress, CIARP 2016. Cham: Springer International Publishing, 2017 - (Beltran-Castanon, C.; Nystrom, I.; Famili, F.), s. 44-51. Lecture Notes in Computer Science, 10125. ISBN 978-3-319-52276-0.
[CIARP 2016 - 21st Iberoamerican Congress 2016. Lima (PE), 08.11.2016-11.11.2016]
R&D Projects: GA ČR(CZ) GA14-10911S
Institutional support: RVO:67985556
Keywords : Texture * texture synthesis * compound random field model * CAR model * two-dimensional Bernoulli mixture * two-dimensional Gaussian mixture * bidirectional texture function
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result website:
http://library.utia.cas.cz/separaty/2017/RO/haindl-0471592.pdf

DOI: https://doi.org/10.1007/978-3-319-52277-7_6

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.
Permanent Link: http://hdl.handle.net/11104/0271351