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Art Innovation B.V., Zutphenstraat 25, 7575 EJ Oldenzaal, The Netherlands


Nationaal Archief, P.O. box 90520, 2509 LM The Hague, The Netherlands


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Abstract Hyperspectral imaging is a non-destructive optical analysis technique that can for instance be used to obtain information from cultural heritage objects unavailable with conventional colour or multi-spectral photography. This technique can be used to distinguish and recognize materials, to enhance the visibility of faint or obscured features, to detect signs of degradation and study the effect of environmental conditions on the object. We describe the basic concept, working principles, construction and performance of a laboratory instrument specifically developed for the analysis of historical documents. The instrument measures calibrated spectral reflectance images at 70 wavelengths ranging from 365 to 1100 nm near-ultraviolet, visible and near-infrared. By using a wavelength tunable narrow-bandwidth light-source, the light energy used to illuminate the measured object is minimal, so that any light-induced degradation can be excluded. Basic analysis of the hyperspectral data includes a qualitative comparison of the spectral images and the extraction of quantitative data such as mean spectral reflectance curves and statistical information from user-defined regions-of-interest. More sophisticated mathematical feature extraction and classification techniques can be used to map areas on the document, where different types of ink had been applied or where one ink shows various degrees of degradation. The developed quantitative hyperspectral imager is currently in use by the Nationaal Archief National Archives of The Netherlands to study degradation effects of artificial samples and original documents, exposed in their permanent exhibition area or stored in their deposit rooms. View Full-Text

Keywords: Spectral imaging; hyperspectral imaging; reflectance imaging; historical document; feature extraction; classification Spectral imaging; hyperspectral imaging; reflectance imaging; historical document; feature extraction; classification

Autor: Marvin E. Klein 1,* , Bernard J. Aalderink 1, Roberto Padoan 2, Gerrit De Bruin 2 and Ted A.G. Steemers 2

Fuente: http://mdpi.com/


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