Watershed segmentation via case-based reasoning (Contributo in volume (capitolo o saggio))

Type
Label
  • Watershed segmentation via case-based reasoning (Contributo in volume (capitolo o saggio)) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-540-75555-5_23 (literal)
Alternative label
  • Frucci M; Perner P; Sanniti di Baja G (2007)
    Watershed segmentation via case-based reasoning
    in Brain, Vision and Artificial Intelligence, 2007
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Frucci M; Perner P; Sanniti di Baja G (literal)
Pagina inizio
  • 244 (literal)
Pagina fine
  • 253 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.springerlink.com/content/e00h014ju13p340u/ (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Brain, Vision and Artificial Intelligence (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 4729 (literal)
Note
  • ACM DL (literal)
  • SpringerLink (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Frucci, Sanniti di Baja: Istituto di cibernetica \"Edoardo Caianiello\" Perner: Institute of Computer Vision and Applied Computer Science, Leipzig, Germany (literal)
Titolo
  • Watershed segmentation via case-based reasoning (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-540-74138-1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • F.Mele; G.Ramella; S.Santillo; F.Ventriglia (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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