Oversegmentation Reduction Via Multiresolution Image Representation

  • Maria Frucci
  • Giuliana Ramella
  • Gabriella Sanniti di Baja
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


We introduce a method to reduce oversegmentation in watershed partitioned images, that is based on the use of a multiresolution representation of the input image. The underlying idea is that the most significant components perceived in the highest resolution image will remain identifiable also at lower resolution. Thus, starting from the image at the highest resolution, we first obtain a multiresolution representation by building a resolution pyramid. Then, we identify the seeds for watershed segmentation on the lower resolution pyramid levels and suitably use them to identify the significant seeds in the highest resolution image. This is finally partitioned by watershed segmentation, providing a satisfactory result. Since different lower resolution levels can be used to identify the seeds, we obtain alternative segmentations of the highest resolution image, so that the user can select the preferred level of detail.


Input Image High Resolution Image Regional Minimum Foreground Region Watershed Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Beucher, S., Lantuejoul, C.: Use of watersheds in contour detection. In: Proc. Int. Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation, Rennes, France (1979)Google Scholar
  2. 2.
    Beucher, S., Meyer, F.: The morphological approach of segmentation: the watershed transformation. In: Dougherty, E. (ed.) Mathematical Morphology in Image Processing, pp. 433–481. M. Dekker, New York (1993)Google Scholar
  3. 3.
    Borgefors, G., Ramella, G., Sanniti di Baja, G.: Shape and topology preserving multi-valued image pyramids for multi-resolution skeletonization. Pattern Recognition Letters 22, 741–751 (2001)zbMATHCrossRefGoogle Scholar
  4. 4.
    Ramella, G., Sanniti di Baja, G.: Grey level image components for multi-scale representation. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds.) CIARP 2004, vol. 3287, pp. 574–581. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Rosenfeld, A. (ed.): Multiresolution Image Processing and Analysis. Springer, Berlin (1984)zbMATHGoogle Scholar
  6. 6.
    Frucci, M.: A novel merging method in watershed segmentation. In: Proc. 4th Indian Conf. on Computer Vision, Graphics, and Image Processing, pp. 532–537. Applied Publishing Private Ltd, Kolkata (2004)Google Scholar
  7. 7.
    Frucci, M., Arcelli, C., Sanniti di Baja, G.: Detecting and Ranking Foreground Regions in Gray-Level Images. In: De Gregorio, M., Di Maio, V., Frucci, M., Musio, C. (eds.) BVAI 2005, vol. 3704, pp. 406–415. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Maria Frucci
    • 1
  • Giuliana Ramella
    • 1
  • Gabriella Sanniti di Baja
    • 1
  1. 1.Institute of Cybernetics ”E.Caianiello”, CNRPozzuoli (Naples)Italy

Personalised recommendations