PHOG: Photometric and Geometric Functions for Textured Shape Retrieval (Articolo in rivista)

Type
Label
  • PHOG: Photometric and Geometric Functions for Textured Shape Retrieval (Articolo in rivista) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1111/cgf.12168 (literal)
Alternative label
  • Biasotti S., Cerri A., Giorgi D., Spagnuolo M. (2013)
    PHOG: Photometric and Geometric Functions for Textured Shape Retrieval
    in Computer graphics forum (Print); Blackwell Publishing, Oxford (Regno Unito)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Biasotti S., Cerri A., Giorgi D., Spagnuolo M. (literal)
Pagina inizio
  • 13 (literal)
Pagina fine
  • 22 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Special Issue Eurographics Symposium on Geometry Processing 2013 Yaron Lipman and Hao Zhang (Guest Editors) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://onlinelibrary.wiley.com/doi/10.1111/cgf.12168/abstract (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 32 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 5 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • PuMa (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-IMATI, Genova, Italy; CNR-IMATI, Genova, Italy; CNR-ISTI, Pisa, Italy; CNR-IMATI, Genova, Italy (literal)
Titolo
  • PHOG: Photometric and Geometric Functions for Textured Shape Retrieval (literal)
Abstract
  • In this paper we target the problem of textured 3D object retrieval. As a first contribution, we show how to include photometric information in the persistence homology setting, also proposing a novel theoretical result about multidimensional persistence spaces. As a second contribution, we introduce a generalization of the integral geodesic distance which fuses shape and color properties. Finally, we adopt a purely geometric description based on the selection of geometric functions that are mutually independent. The photometric, hybrid and geometric descriptions are combined into a signature, whose performance is tested on a benchmark dataset. (literal)
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