A Multiresolution Causal Colour Texture Model

  • Michal Haindl
  • Vojtěch Havlíček
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)

Abstract

An efficient recursive algorithm for realistic colour texture synthesis is proposed. The algorithm starts with spectral factorization of an input colour texture image using the Karhunen-Loeve decorrelation. Single orthogonal monospectral components are further decomposed into a multi-resolution grid and each resolution data are independently modeled by their dedicated simultaneous causal autoregressive random field model (CAR). We estimate an optimal contextual neighbourhood and parameters for each CAR submodel. Finally single synthesized monospectral texture pyramids are collapsed into the fine resolution images and using the inverse Karhunen-Loeve transformation we obtain the required colour texture. The benefit of the multigrid approach is the replacement of a large neighbourhood CAR model with a set of several simpler CAR models which are easy to synthesize and wider application area of these multigrid models capable of reproducing realistic textures for enhancing realism in texture application areas.

Keywords

Texture Synthesis Texture Model Colour Texture Laplacian Pyramid Spectral Factorization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Michal Haindl
    • 1
    • 2
  • Vojtěch Havlíček
    • 2
  1. 1.Center for Machine PerceptionCzech Technical UniversityCzech Republic
  2. 2.Academy of Sciences CRInstitute of Information Theory and AutomationPragueCzech Republic

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