Detecting and Ranking Foreground Regions in Gray-Level Images (Articolo in rivista)

Type
Label
  • Detecting and Ranking Foreground Regions in Gray-Level Images (Articolo in rivista) (literal)
Anno
  • 2005-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/11565123_39 (literal)
Alternative label
  • Frucci M; Arcelli C; Sanniti di Baja G (2005)
    Detecting and Ranking Foreground Regions in Gray-Level Images
    in Lecture notes in computer science; Springer-Verlag, Berlin/Heidelberg (Germania)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Frucci M; Arcelli C; Sanniti di Baja G (literal)
Pagina inizio
  • 406 (literal)
Pagina fine
  • 415 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.springerlink.com/content/u185051216t0tu36/ (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 3704 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • Viene descritto un metodo per la binarizzazione di immagini 2D grey-level sulla base delle caratteristiche e relazioni spaziali delle regioni individuate dalla watershed trasformation (literal)
Note
  • ACM DL (literal)
  • SpringerLink (literal)
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto di Cibernetica \"Edoardo Caianiello\" (literal)
Titolo
  • Detecting and Ranking Foreground Regions in Gray-Level Images (literal)
Abstract
  • Starting from a gray-level image partitioned into regions by watershed segmentation, we introduce a method to assign the regions to the foreground and the background, respectively. The method is inspired by visual perception and identifies the border between foreground and background in correspondence with the locally maximal changes in gray-level. The obtained image representation is hierarchical, both due to the articulation of the assignment process into three steps, aimed at the identification of components of the foreground with decreasing perceptual relevance, and due to a parameter taking into account the distance of each foreground region from the most relevant part in the same foreground component. Foreground components are detected by resorting to both global and local processes. Global assignment, cheaper from a computational point of view, is accomplished as far as this can be safely done. Local assignment takes place in the presence of conflictual decisions. (literal)
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