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
  • 39 (literal)
Pagina fine
  • 44 (literal)
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
  • 462 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 6 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • IFAC-CNR (literal)
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
  • 1558993665 (literal)
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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