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

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.

A new phytocosmetic preparation from Thymus vulgaris stimulates adipogenesis and controls skin aging process: In vitro studies and topical effects in a double‐blind placebo‐controlled clinical trial

“…Briefly, the Bio3D Structured‐light Scanner is a 3D (three dimensions) digitalization system based on structured‐light fringe projection, a technique called GCPS (Gray‐code and phase‐shift), which works at 290 images per second, reducing the effects of movement and getting a high resolution. The 3D information obtained was analyzed with specific software, developed internally to perform different quantifications of skin surface and profile 19 . The projector used was a high‐performance and high‐reliability device, with a very small size that uses DLP (data loss prevention) technology and a high‐power white LED (light‐emitting diode) light source with a frame rate of 290 fps (frames per second).…”

“…The 3D information obtained was analyzed with specific software, developed internally to perform different quantifications of skin surface and profile. 19 The projector used was a high-performance and high-reliability device, with a very small size that uses DLP (data loss prevention) technology and a highpower white LED (light-emitting diode) light source with a frame rate of 290 fps (frames per second). The camera contains a 1.3 megapixels CMOS (complementary metal-oxide-semiconductor) sensor with GigE (gigabit ethernet) Interface for high-speed optical metrology applications.…”

Nondestructive Evaluation of Heritage Object Coatings with Four Hyperspectral Imaging Systems

“…However, an important technical challenge is to ensure imaging with an adequate signal-to-noise ratio in low-light conditions, such as those encountered in most restoration laboratories or museums. Long exposure time, even if beneficial for spectral resolution, can be problematic in the evaluation of art objects due to their sensitivity to deterioration [22].…”

“…However, large training datasets are needed to successfully explore convolutional neural networks (CNNs). New methods for acquiring high-resolution hyperspectral images [25][26][27] or new algorithms for post-processing spectral data [22,[28][29][30][31][32][33] are currently of interest to engineers and researchers working in this field. Similarly, multisensor systems that allow automatic characterisation routines of art objects with high accuracy and precision are desired [34].…”