Neural model-based segmentation of image motion (Contributo in atti di convegno)

Type
Label
  • Neural model-based segmentation of image motion (Contributo in atti di convegno) (literal)
Anno
  • 2008-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-540-85563-7-13 (literal)
Alternative label
  • Maddalena, Lucia; Petrosino, Alfredo (2008)
    Neural model-based segmentation of image motion
    in Knowledge-Based Intelligent Information and Engineering Systems, 12th International Conference, KES 2008,, Zagreb, 3-5 September 2008
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Maddalena, Lucia; Petrosino, Alfredo (literal)
Pagina inizio
  • 57 (literal)
Pagina fine
  • 64 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • KES 2008 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 5177 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 5177 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 8 (literal)
Note
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Consiglio Nazionale delle Ricerche (CNR) (literal)
Titolo
  • Neural model-based segmentation of image motion (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-540-85562-0 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • I. Lovrek, R.J. Howlett, and L.C. Jain (literal)
Abstract
  • Besides enabling the segmentation of video streams into moving and background components, detecting moving objects provides a focus, of attention for recognition, classification, and activity analysis, making these later steps more efficient. We propose a novel model for image sequences based on self organization through artificial neural networks, that is used both for background modeling, allowing to handle scenes containing moving backgrounds or gradual illumination variations, and for stopped foreground modeling, helping ill distinguishing between moving and stopped foreground regions and leading to an initial segmentation of scene objects. Experimental results are presented for real video sequences. (literal)
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