Two Compound Random Field Texture Models

  • Michal HaindlEmail author
  • Vojtěch Havlíček
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10125)


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.


Texture Texture synthesis Compound random field model CAR model Two-dimensional Bernoulli mixture Two-dimensional Gaussian mixture Bidirectional texture function 



This research was supported by the Czech Science Foundation project GAČR 14-10911S.


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.The Institute of Information Theory and Automation of the Czech Academy of SciencesPragueCzech Republic

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