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Region Detection in Images

  • Chapter
Self-organising Software

Part of the book series: Natural Computing Series ((NCS))

Abstract

This chapter presents an application of a stigmergic approach to extract regions in grey-level images. This application is based on the model of a social spiders behaviour which has been presented earlier in Chap. 6. This chapter first introduces the region detection problem, justifies the interest of a multi-agent application for this issue, presents the transposition of the spider model and shows the results obtained by this approach.

How a stigmergic process based on silk in social spiders can be transposed to detect regions in grey-level images.

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Notes

  1. 1.

    The distance between two pixels of respective coordinates (x 1,y 1) and (x 2,y 2) is ‖x 1−x 2‖+‖y 1−y 2‖.

  2. 2.

    From now on, this case will be referred as ‘several regions detection’.

  3. 3.

    In our experiments, we used f(x)=min (x,SaturationValue) where SaturationValue corresponds to a limit from which the number of draglines does not any longer influence the result.

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Acknowledgements

We wish to acknowledge all the students who worked on the several versions of the software, especially Aurélien Saint-Dizier, Dominique Marie and Anne Chevreux.

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Authors and Affiliations

  1. LORIA, Université Nancy, Nancy, France

    Vincent Chevrier, Christine Bourjot & Vincent Thomas

Authors
  1. Vincent Chevrier
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  2. Christine Bourjot
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  3. Vincent Thomas
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Corresponding author

Correspondence to Vincent Chevrier .

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Editors and Affiliations

  1. Centre Universitaire d'Informatique, Université de Genève, route de Drize 7, Carouge, 1227, Geneve, Switzerland

    Giovanna Di Marzo Serugendo

  2. , Institut de Recherche, Université Paul Sabatier, route de Narbonne 118, Toulouse, 31062, France

    Marie-Pierre Gleizes

  3. of Larissa, Dept. of Forestry, Technological Education Institute, Terma Mavromihali, Karditsa, 431 00, Greece

    Anthony Karageorgos

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Chevrier, V., Bourjot, C., Thomas, V. (2011). Region Detection in Images. In: Di Marzo Serugendo, G., Gleizes, MP., Karageorgos, A. (eds) Self-organising Software. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17348-6_17

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  • DOI: https://doi.org/10.1007/978-3-642-17348-6_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17347-9

  • Online ISBN: 978-3-642-17348-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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