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Internatinoal Workshop on computational Intelligence for Multimedia Understanding

MUSCLE 2011: Computational Intelligence for Multimedia Understanding pp 138–148Cite as

A Plausible Texture Enlargement and Editing Compound Markovian Model

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7252))

Abstract

This paper describes high visual quality compound Markov random field texture model capable to realistically model multispectral bidirectional texture function, which is currently the most advanced representation of visual properties of surface materials. The presented compound Markov random field model combines a non-parametric control random field with analytically solvable wide-sense Markov representation for single regions and thus allows very efficient non-iterative parameters estimation as well as the compound random field synthesis. The compound Markov random field model is utilized for realistic texture compression, enlargement, and powerful automatic texture editing. Edited textures maintain their original layout but adopt anticipated local characteristics from one or several parent target textures.

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Author information

Authors and Affiliations

  1. Institute of Information Theory and Automation, of the ASCR, Prague, Czech Republic

    Michal Haindl & Vojtěch Havlíček

Authors
  1. Michal Haindl
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  2. Vojtěch Havlíček
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Editor information

Editors and Affiliations

  1. ISTI, National Research Council of Italy, Via Moruzzi, 1, 56124, Pisa, Italy

    Emanuele Salerno  & Ovidio Salvetti  & 

  2. Electrical and Electronics Engineering, Bilkent University, 06800, Bilkent, Ankara, Turkey

    A. Enis Çetin

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© 2012 Springer-Verlag Berlin Heidelberg

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Haindl, M., Havlíček, V. (2012). A Plausible Texture Enlargement and Editing Compound Markovian Model. In: Salerno, E., Çetin, A.E., Salvetti, O. (eds) Computational Intelligence for Multimedia Understanding. MUSCLE 2011. Lecture Notes in Computer Science, vol 7252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32436-9_12

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  • DOI: https://doi.org/10.1007/978-3-642-32436-9_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32435-2

  • Online ISBN: 978-3-642-32436-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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