Multiscale Morphological Image Simplification

  • Leyza Baldo Dorini
  • Neucimar Jerônimo Leite
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)


Image simplification reduces the information content of an image, being frequently used as a preprocessing stage in several algorithms to suppress undesired details such as noise. Morphological filters, commonly used for this purpose, have as main drawbacks the asymmetric treatment of peaks and valleys and the difficulty to choose an appropriate structuring element size. Here, we propose a self-dual multiscale image simplification operator with sound edge preservation properties. This enables us to represent the inherent multiscale nature of real-world images by embedding the original signal into a family of derived signals, which represent simplified versions of the image obtained by successively removing its structures across scales. Thus, it is possible to analyze the different representation levels to extract the interest features, and the definition of a structure element size does not constitute a problem anymore. Based on these notions, we present some experiments on image segmentation, a basic step of various pattern recognition approaches.


mathematical morphology multiscale analysis image segmentation image simplification 


  1. 1.
    Beucher, S., Meyer, F.: The morphological approach to segmentation: the watershed transformation. In: Mathematical Morphology in Image Processing, pp. 433–451. Marcel Dekker, New York (1993)Google Scholar
  2. 2.
    Meyer, F.: Levelings, image simplification filters for segmentation. Journal of Mathematical Imaging and Vision 20, 59–72 (2004)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Salembier, P., Serra, J.: Flat zones filtering, connected operators, and filters by reconstruction. IEEE Transactions on Image Processing, 1153–1160 (1995)Google Scholar
  4. 4.
    Serra, J.: Image Analysis and Mathematical Morphology. Theoretical Advances, vol. 2. Academic Press, London (1988)Google Scholar
  5. 5.
    Heijmans, H., van den Boomgaard, R.: Algebraic framework for linear and morphological scale-spaces. Journal of Visual Communication and Image Representation 13, 269–301 (2002)CrossRefGoogle Scholar
  6. 6.
    Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  7. 7.
    Vincent, L.: Grayscale area openings and closings: their applications and efficient implementation. In: EURASIP Workshop on Mathematical Morphology and its Applications to Signal Processing, pp. 22–27 (1993)Google Scholar
  8. 8.
    Jackway, P.T., Deriche, M.: Scale-space properties of the multiscale morphological dilation-erosion. PAMI 18, 38–51 (1996)CrossRefGoogle Scholar
  9. 9.
    Dorini, L.E.B., Leite, N.J.: A scale-space toggle operator for morphological segmentation. In: 8th ISMM, pp. 101–112 (2007)Google Scholar
  10. 10.
    Parker, J.R.: Algorithms for Image Processing and Computer Vision. Wiley, Chichester (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Leyza Baldo Dorini
    • 1
  • Neucimar Jerônimo Leite
    • 1
  1. 1.Institute of ComputingUniversity of Campinas - UNICAMPCampinasBrazil

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