A Basin Morphology Approach to Colour Image Segmentation by Region Merging

  • Erchan Aptoula
  • Sébastien Lefèvre
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)

Abstract

The problem of colour image segmentation is investigated in the context of mathematical morphology. Morphological operators are extended to colour images by means of a lexicographical ordering in a polar colour space, which are then employed in the preprocessing stage. The actual segmentation is based on the use of the watershed transformation, followed by region merging, with the procedure being formalized as a basin morphology, where regions are “eroded” in order to form greater catchment basins. The result is a fully automated processing chain, with multiple levels of parametrisation and flexibility, the application of which is illustrated by means of the Berkeley segmentation dataset.

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References

  1. 1.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the 8th International Conference on Computer Vision, Vancouver, Canada, vol. 2, pp. 416–423 (2001)Google Scholar
  2. 2.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley, New York (1992)Google Scholar
  3. 3.
    Hanbury, A., Serra, J.: Colour image analysis in 3d-polar coordinates. In: International Conference on Image Processing and its Applications, Magdeburg, Germany (2003)Google Scholar
  4. 4.
    Serra, J.: Image Analysis and Mathematical Morphology, vol. I. Academic Press, London (1982)MATHGoogle Scholar
  5. 5.
    Ronse, C.: Why mathematical morphology needs complete lattices. Signal Processing 21(2), 129–154 (1990)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Aptoula, E., Lefèvre, S.: A comparative study on multivariate mathematical morphology. Pattern Recognition  (2007), doi:10.1016/j.patcog.2007.02.004).Google Scholar
  7. 7.
    Hanbury, A., Serra, J.: Morphological operators on the unit circle. IEEE Transactions on Image Processing 10(12), 1842–1850 (2001)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Gomila, C., Meyer, F.: Levelings in vector spaces. In: Proceedings of the IEEE Conference on Image Processing, Kobe, Japan (1999)Google Scholar
  9. 9.
    Chen, Q., Zhou, C., Luo, J., Ming, D.: Fast segmentation of high-resolution satellite images using watershed transform combined with an efficient region merging approach. In: Klette, R., Žunić, J. (eds.) IWCIA 2004. LNCS, pp. 621–630. Springer, Heidelberg (2004)Google Scholar
  10. 10.
    Garrido, L., Salembier, P., Garcia, D.: Extensive operators in partition lattices for image sequence analysis. Signal Processing 66(2), 157–180 (1998)MATHCrossRefGoogle Scholar
  11. 11.
    Angulo, J., Serra, J.: Modelling and segmentation of colour images in polar representations. Image and Vision Computing  (2006), doi:10.1016/j.imavis.2006.07.018).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Erchan Aptoula
    • 1
  • Sébastien Lefèvre
    • 1
  1. 1.UMR-7005 CNRS-Louis Pasteur University, LSIIT, Pôle API, Bvd Brant, PO Box 10413, 67412 Illkirch CedexFrance

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