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Imaging data integration for painting diagnostics

  Articoli su Riviste JCR/ISI  (anno 2009)

Autori:  Daffara C., Ambrosini D., Di Biase R., Fontana R., Paoletti D., Pezzati L., Rossi S

Affiliazione Autori:  CNR - Istituto Nazionale di Ottica Applicata, Largo E. Fermi 6, I-50125 Firenze, Italy; DIMEG, Università dell\'Aquila, Piazzale E. Pontieri 1, I-67100 Monteluco di Roio, L\'Aquila, Italy; Soprintendenza Speciale PSAE e per il Polo Museale Veneziano, Piazza San Marco 63, I-30124, Venezia, Italy

Riassunto:  In the field of art conservation non-invasive techniques based on imaging in different spectral regions are widely used for investigation of paintings. Using radiation beyond the visible range, different characteristics of the inspected artwork may be revealed according to the bandwidth acquired. Beyond the traditional diagnostic methods, such as reflectography, thermography, selective multi-spectral analysis in the near-infrared region has been recently demonstrated to be a promising tool for investigating pictorial layers. In this work we present the results of a multidisciplinary collaboration among two research institutes and the Accademia Galleries of Venice concerning an integrated approach for multi-view and multi-spectral imaging data analysis for the diagnostics of paintings. In order to perform this integrated analysis, a graphical user interface with options such as image adjustment, overlaying and transparency variation was designed. The effectiveness of this integrated approach is recognized by the operators in the field of conservation that may thus have at their disposal the complete set of information spanning the different characteristics of the object under investigation. Data integration provides a multi-layered and multi-spectral representation of the painting that yields a comprehensive analysis, confirms the anomalies individuation and reduces the ambiguity of information coming from a single diagnostic method.

Rivista/Giornale:  PROCEEDINGS OF SPIE
Volume n.:  7391      Pagine da: 73910X  a: 73910X
DOI: 10.1117/12.827710

   *Citazioni: 1
data tratti da "WEB OF SCIENCE" (marchio registrato di Thomson Reuters) ed aggiornati a:  19/05/2019

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