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3D Multi-frequency Fully Correlated Causal Random Field Texture Model

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Pattern Recognition (ACPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12047))

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Abstract

We propose a fast novel multispectral texture model with an analytical solution for both parameter estimation as well as unlimited synthesis. This Gaussian random field type of model combines a principal random field containing measured multispectral pixels with an auxiliary random field resulting from a given function whose argument is the principal field data. The model can serve as a stand-alone texture model or a local model for more complex compound random field or bidirectional texture function models. The model can be beneficial not only for texture synthesis, enlargement, editing, or compression but also for high accuracy texture recognition.

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Acknowledgments

The Czech Science Foundation project GAČR 19-12340S supported this research.

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Authors and Affiliations

  1. Institute of Information Theory and Automation, The Czech Academy of Sciences, Prague, Czechia

    Michal Haindl & Vojtěch Havlíček

  2. Faculty of Management, University of Economics, Jindřichův Hradec, Czechia

    Michal Haindl

Authors
  1. Michal Haindl
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  2. Vojtěch Havlíček
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Corresponding author

Correspondence to Michal Haindl .

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Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Shivakumara Palaiahnakote

  2. Consiglio Nazionale delle Ricerche, ICAR, Naples, Italy

    Gabriella Sanniti di Baja

  3. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  4. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

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Haindl, M., Havlíček, V. (2020). 3D Multi-frequency Fully Correlated Causal Random Field Texture Model. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_33

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  • DOI: https://doi.org/10.1007/978-3-030-41299-9_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41298-2

  • Online ISBN: 978-3-030-41299-9

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