http://www.cnr.it/ontology/cnr/individuo/prodotto/ID226771
Principal Component Analysis of Spectral Data: A Contribution to the Knowledge of the Materials Constituting Works of Art (Contributo in atti di convegno)
- Type
- Label
- Principal Component Analysis of Spectral Data: A Contribution to the Knowledge of the Materials Constituting Works of Art (Contributo in atti di convegno) (literal)
- Anno
- 1997-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1557/PROC-462-39 (literal)
- Alternative label
M. Bacci, S. Baronti, A. Casini, F. Lotti, M. Picollo, S. Porcinai (1997)
Principal Component Analysis of Spectral Data: A Contribution to the Knowledge of the Materials Constituting Works of Art
in 1996 MRS Fall Meeting
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- M. Bacci, S. Baronti, A. Casini, F. Lotti, M. Picollo, S. Porcinai (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=8142445 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
- Materials Issues in Art and Archaeology V (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Titolo
- Principal Component Analysis of Spectral Data: A Contribution to the Knowledge of the Materials Constituting Works of Art (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
- J.R. Druzik, J. Merkel, J. Stewart, P.B. Vandiver (literal)
- Abstract
- The use of totally non-destructive techniques such as image spectroscopy for diagnosing paintings makes it possible to obtain a large amount of spectral data that provides information concerning the composition of works of art. Here, we stress how statistical treatments, such as principal component analysis (PCA), applied to 2-D data, can contribute to a better knowledge of the work of art itself and of the distribution of the materials that constitute it. (literal)
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