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Model-Based Texture Segmentation

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Book cover Image Analysis and Recognition (ICIAR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3212))

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Abstract

An efficient and robust type of unsupervised multispectral texture segmentation method is presented. Single decorrelated monospectral texture factors are assumed to be represented by a set of local Gaussian Markov random field (GMRF) models evaluated for each pixel centered image window and for each spectral band. The segmentation algorithm based on the underlying Gaussian mixture (GM) model operates in the decorrelated GMRF parametric space. The algorithm starts with an oversegmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached.

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Author information

Authors and Affiliations

  1. Institute of Information Theory and Automation, Academy of Sciences CR, 182 08, Prague, Czech Republic

    Michal Haindl & Stanislav Mikeš

Authors
  1. Michal Haindl
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  2. Stanislav Mikeš
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Editor information

Editors and Affiliations

  1. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

  2. Electrical and Computer Engineering Department, University of Waterloo,  

    Mohamed Kamel

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© 2004 Springer-Verlag Berlin Heidelberg

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Haindl, M., Mikeš, S. (2004). Model-Based Texture Segmentation. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30126-4_38

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  • DOI: https://doi.org/10.1007/978-3-540-30126-4_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23240-7

  • Online ISBN: 978-3-540-30126-4

  • eBook Packages: Springer Book Archive

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