Permanence-Based Shape Decomposition in Binary Pyramids (Contributo in volume (capitolo o saggio))

Type
Label
  • Permanence-Based Shape Decomposition in Binary Pyramids (Contributo in volume (capitolo o saggio)) (literal)
Anno
  • 1999-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/ICIAP.1999.797568 (literal)
Alternative label
  • G. Borgefors 1, G. Ramella 2, G. Sanniti di Baja 2 (1999)
    Permanence-Based Shape Decomposition in Binary Pyramids
    IEEE Computer Society, Los Alamitos [CA] (Stati Uniti d'America) in Image Analysis and Processing, 1999. Proceedings. International Conference on, 1999
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • G. Borgefors 1, G. Ramella 2, G. Sanniti di Baja 2 (literal)
Pagina inizio
  • 38 (literal)
Pagina fine
  • 43 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Proceedings 10th International Conference on Image Analysis and Processing (ICIAP 99) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#citta
  • Los Alamitos (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://doi.ieeecomputersociety.org/10.1109/ICIAP.1999.797568 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Image Analysis and Processing, 1999. Proceedings. International Conference on (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 6 (literal)
Note
  • IEEE Xplore digital library (literal)
  • Google Scholar (literal)
  • The Collection of Computer Science Bibliography (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1 Centre for Image Analysis, Uppsala, Svezia 2 Istituto di Cibernetica \"E. Caianiello\" - CNR (literal)
Titolo
  • Permanence-Based Shape Decomposition in Binary Pyramids (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#inCollana
  • Image Analysis and Processing 1999 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 0-7695-0040-4 (literal)
Abstract
  • An algorithm to decompose hierarchically bidimensional patterns is introduced. The single-scale input pattern is first transformed into a multi-scale data set. The multi-resolution skeleton is then computed and its hierarchical decomposition is obtained by using the notion of permanence. A constrained reverse distance transformation is applied to the skeleton components to reconstruct the regions into which the pattern is decomposed. A merging process then reduces the number of components to the most significant ones and improves decomposition stability. (literal)
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