http://www.cnr.it/ontology/cnr/individuo/prodotto/ID250374
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
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- 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)
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- http://onlinelibrary.wiley.com/doi/10.1111/cgf.12168/abstract (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
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- 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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