Damage and deterioration monitoring of artwork by data fusion of 3D surface and hyperspectral measurements

Roger M. Groves; José Caballero; Ianisse Quinzan; Emilio Ribes-Gómez

doi:10.1117/12.2051898

15 May 2014 Damage and deterioration monitoring of artwork by data fusion of 3D surface and hyperspectral measurements

Roger M. Groves, José Caballero, Ianisse Quinzan, Emilio Ribes-Gómez

Proceedings Volume 9141, Optical Sensing and Detection III; 91411E (2014) https://doi.org/10.1117/12.2051898
Event: SPIE Photonics Europe, 2014, Brussels, Belgium

Abstract

This paper describes the processing algorithm methodology and preliminary results from a novel optical-based system for the assessment of chemical and mechanical deterioration of artworks. The FP7 Syddarta Project prototype is composed of two optical channels: 1) a 3D imaging channel which acquires 3D surface data and multiband information in the visible spectral range; 2) an infrared hyperspectral imaging channel in the spectral range 900 to 2500 nm. The processing algorithms developed perform the system calibration, damage detection and chemical deterioration analysis. Both photometric and geometric calibrations have been implemented. The photometric calibration is based on a white reference and intensity map and compensates for variation in light intensities. The geometric calibration is based on planar homographies to determine the interior and exterior orientation of the projector and the two cameras. This is used to map the acquired data of the different sensors into a single reference frame. To acquire 3D data, a set of phase-shifted fringe patterns is projected on the object which are processed by Fourier transform. To identify mechanical deterioration, the acquired 3D cloud of points is meshed and differences in surface normals for a given radius are computed. To analyse the chemical deterioration of the pigments a supervised classification method has been implemented. First of all, spectral data is normalized with the Extended Multiplicative Scatter Correction algorithm. Then, data dimensionality is reduced by applying Principal Component Analysis and classification is done with Support Vector Machine. Results are presented showing the performance of the described algorithms.

Citation Download Citation

Roger M. Groves, José Caballero, Ianisse Quinzan, and Emilio Ribes-Gómez "Damage and deterioration monitoring of artwork by data fusion of 3D surface and hyperspectral measurements", Proc. SPIE 9141, Optical Sensing and Detection III, 91411E (15 May 2014); https://doi.org/10.1117/12.2051898

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