Making image segmentation fully automatic by case-based-reasoning (Contributo in atti di convegno)

Type
Label
  • Making image segmentation fully automatic by case-based-reasoning (Contributo in atti di convegno) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Alternative label
  • Frucci M; Perner P; Sanniti di Baja G. (2009)
    Making image segmentation fully automatic by case-based-reasoning
    in 10th International Conference on Pattern Recognition and Information Processing, PRIP09, Minsky, Belarus, May 19-21, 2009
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Frucci M; Perner P; Sanniti di Baja G. (literal)
Pagina inizio
  • 26 (literal)
Pagina fine
  • 30 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Proc. of the 10th International Conference on Pattern Recognition and Information Processing, PRIP09 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto di Cibernetica \"E. Caianiello\", CNR institute of Computer Vision and Applied Computer Science, Leipzig, Germany (literal)
Titolo
  • Making image segmentation fully automatic by case-based-reasoning (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-985-476-704-8 (literal)
Abstract
  • Image segmentation methods involve a number of parameters whose values have to be tuned depending on image domain. In this communication, a watershedbased segmentation algorithm is considered and Case-Based-Reasoning is used for the automatic selection of the values that, assigned to the parameters, produce a satisfactory segmentation. In this way, the segmentation algorithm can be applied to a wider image domain. (literal)
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