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Internatinoal Workshop on computational Intelligence for Multimedia Understanding

MUSCLE 2011: Computational Intelligence for Multimedia Understanding pp 149–159Cite as

Bidirectional Texture Function Simultaneous Autoregressive Model

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

Abstract

The Bidirectional Texture Function (BTF) is the recent most advanced representation of visual properties of surface materials. It specifies their altering appearance due to varying illumination and viewing conditions. Corresponding huge BTF measurements require a mathematical representation allowing simultaneously extremal compression as well as high visual fidelity. We present a novel Markovian BTF model based on a set of underlying simultaneous autoregressive models (SAR). This complex but efficient BTF-SAR model combines several multispectral band limited spatial factors and range map sub-models to produce the required BTF texture space. The BTF-SAR model enables very high BTF space compression ratio, texture enlargement, and reconstruction of missing unmeasured parts of the BTF space.

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Authors and Affiliations

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

    Michal Haindl & Michal Havlíček

Authors
  1. Michal Haindl
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  2. Michal 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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Haindl, M., Havlíček, M. (2012). Bidirectional Texture Function Simultaneous Autoregressive 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_13

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  • DOI: https://doi.org/10.1007/978-3-642-32436-9_13

  • 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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