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Watershed Segmentation Via Case-Based Reasoning

  • Maria Frucci
  • Petra Perner
  • Gabriella Sanniti di Baja
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4729)

Abstract

This paper proposes a novel grey-level image segmentation scheme employing case-based reasoning. Segmentation is accomplished by using the watershed transformation, which provides a partition of the image into regions whose contours closely fit those perceived by human users. Case-based reasoning is used to select the segmentation parameters involved in the segmentation algorithm by taking into account the features characterizing the current image. Preliminarily, a number of images are analyzed and the parameters producing the best segmentation for each image, found empirically, are recorded. These images are grouped to form relevant cases, where each case includes all images having similar image features, under the assumption that the same segmentation parameters will produce similarly good segmentation results for all images in the case.

Keywords

Image Segmentation Current Image Gradient Image Good Segmentation 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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Maria Frucci
    • 1
  • Petra Perner
    • 2
  • Gabriella Sanniti di Baja
    • 1
  1. 1.Institute of Cybernetics "E.Caianiello", CNR, PozzuoliItaly
  2. 2.Institute of Computer Vision and Applied Computer Science, LeipzigGermany

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