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Segmentation Algorithm by Multicriteria Region Merging

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Mathematical Morphology and its Applications to Image and Signal Processing

Part of the book series: Computational Imaging and Vision ((CIVI,volume 5))

Abstract

This paper presents a segmentation algorithm for image sequences, by multicriteria region merging. The output of a connected filter is simplified by iteratively merging the two most similar adjacent regions, while a given representation quality is preserved. We have defined several region-similitude criteria: grey-level, texture and motion resemblance. Texture and motion criteria introduce a feedback “segmentation-coding step” that improves coding efficiency.

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References

  1. LEP. First results of motion prediction based on a differential estimation method. In SIM(94) 35 COST 211, Tampere, June 1994.

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  2. B. Marcotegui, J. Crespo, and F. Meyer. Morphological segmentation using texture and coding cost. IEEE Workshop on Non Linear Signal/Image Processing, Juin 1995.

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Author information

Authors and Affiliations

  1. Ecole des Mines de Paris, Centre de Morphologie Mathématique, 35, rue Saint-Honoré, 77305, Fontainebleau Cedex, France

    B. Marcotegui

Authors
  1. B. Marcotegui
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Editor information

Editors and Affiliations

  1. Georgia Institute of Technology, USA

    Petros Maragos , Ronald W. Schafer  & Muhammad Akmal Butt ,  & 

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© 1996 Kluwer Academic Publishers

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Marcotegui, B. (1996). Segmentation Algorithm by Multicriteria Region Merging. In: Maragos, P., Schafer, R.W., Butt, M.A. (eds) Mathematical Morphology and its Applications to Image and Signal Processing. Computational Imaging and Vision, vol 5. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0469-2_36

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  • DOI: https://doi.org/10.1007/978-1-4613-0469-2_36

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-8063-4

  • Online ISBN: 978-1-4613-0469-2

  • eBook Packages: Springer Book Archive

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