In this paper we present a scanning device for multispectral imaging of paintings in the 380-800 nm spectral region; the system is based on a spectrophotometer for contact-less single-point measurements of the spectral reflectance with 10 nm resolution. Two orthogonal XY translation stages allow to scan up to 1,5 m2 with spatial resolution up to 8 dots/mm. As an application we present the results of the measurements carried out on Ritratto Trivulzio by Antonello da Messina and Madonna in gloria tra Santi by Andrea Mantegna. Besides spectra comparison also multivariate image analyses (MIA) have been performed by considering the multi-spectral images as three-way data set. In order to point out the slight spectral differences of two areas of a painting we analyzed its multispectral data cube by means of the Principal Component Analysis (PCA) and the K-Nearest-Neighbouring Cluster Analysis (KNN).

Multispectral imaging of paintings: instrument and applications

Fontana R;Greco M;Materazzi M;Pampaloni E;Pezzati L;
2007

Abstract

In this paper we present a scanning device for multispectral imaging of paintings in the 380-800 nm spectral region; the system is based on a spectrophotometer for contact-less single-point measurements of the spectral reflectance with 10 nm resolution. Two orthogonal XY translation stages allow to scan up to 1,5 m2 with spatial resolution up to 8 dots/mm. As an application we present the results of the measurements carried out on Ritratto Trivulzio by Antonello da Messina and Madonna in gloria tra Santi by Andrea Mantegna. Besides spectra comparison also multivariate image analyses (MIA) have been performed by considering the multi-spectral images as three-way data set. In order to point out the slight spectral differences of two areas of a painting we analyzed its multispectral data cube by means of the Principal Component Analysis (PCA) and the K-Nearest-Neighbouring Cluster Analysis (KNN).
2007
Istituto Nazionale di Ottica - INO
multi-spectral imaging
scanning device
imaging analysis
Principal Component Analysis (PCA)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/24030
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