Watershed segmentation via case-based reasoning (Articolo in rivista)

Type
Label
  • Watershed segmentation via case-based reasoning (Articolo in rivista) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Alternative label
  • M. Frucci, P. Perner, G. Sanniti di Baja (2007)
    Watershed segmentation via case-based reasoning
    in Lecture notes in computer science
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • M. Frucci, P. Perner, G. Sanniti di Baja (literal)
Pagina inizio
  • 244 (literal)
Pagina fine
  • 253 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 4729 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • Viene mostrato come l'uso del case-based-reasoning permetta di migliorare la performance di un algoritmo di segmentazione basato su watershed transformation (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto di cibernetica \"Edoardo Caianiello\" Institute of Computer Vision and Applied Computer Science, Leipzig, Germany (literal)
Titolo
  • Watershed segmentation via case-based reasoning (literal)
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. (literal)
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