Automatic pigment identification from hyperspectral data

Art objects conservation or historical analysis necessitates a thorough knowledge of materials used by the artist and their subsequent changes. In the case of paintings this requires the ability to correctly identify the pigments that were used for creation or later restoration of the artwork. This is a challenging problem, as the applied method should be non-contact, robust for the wide variety of chemical substances used and straightforward in the interpretation. Recently, the hyperspectral imaging has emerged as a promising measuring methodology for this kind of the artwork analysis; the combination of acquiring spectral information and planar (photography-like) pixel arrangement provides a lot of potential for material characterization. While initial studies of hyperspectral imaging application to art objects analysis are encouraging, the difficulties of working with its multidimensional data are acknowledged; in many cases complex algorithms are required to fully utilize its potential. In this paper, we study the problem of algorithm design for pigment identification based on a hyperspectral image of a painting. We combine various processing steps to achieve a robust solution requiring minimal user intervention. Using a special set of paintings and a reference pigment database we demonstrate the viability of applying this method in the pigment recognition setting. Our results confirm the potential of using hyperspectral imaging in the art conservation setting, and based on them we discuss the potential construction and elements of such an algorithm. (C) 2018 Les Auteurs. Publie par Elsevier Masson SAS.