Search Results (7)

Wood decayed and colored by fungi, colloquially known as ‘spalted wood’, has been a source of art and folklore across numerous cultures. From intarsia and marquetry in Italy and Germany to woodturning in the U.S. and carving and mythology in Chile, the uses of, and stories about, spalted wood are explored, as well as how those have shaped their surrounding cultures as well as modern science. Full article

The use of infrared thermography presents unique perspectives in imaging of artifacts to help interrogate their surface and subsurface characteristics, highlight deviations and detect contrast. This research capitalizes on active and passive thermal imagery along with advanced machine learning-based algorithms for pre- and post-processing of acquired scans. Such codes operate efficiently (compress data) to help link the observed temperature variations and the thermophysical parameters of targeted samples. One such processing modality is dictionary learning, which infers a “frame dictionary” to help represent the scans as linear combinations of a small set of features, thus training data to show a sparse representation. This technique (along factorization and component analysis-based methods) was used in current research on ancient polychrome marquetries aimed at detecting aging anomalies. The presented research is unique in terms of the targeted samples and the applied approaches and should provide specific guidance to similar domains. Full article

Non-destructive testing of objects and structures is a valuable tool, especially in cultural heritage where the preservation of the inspected sample is of vital importance. In this paper, a decorative marquetry sample is inspected with three non-destructive testing (NDT) techniques: air-coupled ultrasound, X-ray micro-tomography, and infrared thermography. Results from the three techniques were compared and discussed. X-ray micro-tomography presented the most detailed results. On the other hand, infrared thermography provided interesting results with the advantage of being cheap and easy in the deployment of the NDT method. Full article

The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.e., undisturbed, non-contact and fast inspection of large surfaces) and its continuous evolution in both the acquisition and the processing of the data acquired. Despite the good qualitative and quantitative results obtained so far, the lack of automation in the IRT data interpretation predominates, with few automatic analyses that are limited to specific conditions and the technology of the thermographic camera. Deep Learning (DL) is a data processor with a versatile solution for highly automated analysis. Then, this paper introduces the latest state-of-the-art DL model for instance segmentation, Mask Region-Convolution Neural Network (Mask R-CNN), for the automatic detection and segmentation of the position and area of different surface and subsurface defects, respectively, in two different artistic objects belonging to the same family: Marquetry. For that, active IRT experiments are applied to each marquetry. The thermal image sequences acquired are used as input dataset in the Mask R-CNN learning process. Previously, two automatic thermal image pre-processing algorithms based on thermal fundamentals are applied to the acquired data in order to improve the contrast between defective and sound areas. Good detection and segmentation results are obtained regarding state-of-the-art IRT data processing algorithms, which experience difficulty in identifying the deepest defects in the tests. In addition, the performance of the Mask R-CNN is improved by the prior application of the proposed pre-processing algorithms. Full article

Natural wood textures are appreciated in most forest products industries for their appealing visual characteristics including grain and color, but also their fine surface tactile sensation. The following presents an ultraviolet (UV)-curable inkjet technology printing 3D wood texture on wood-based substrate by image processing and surface treatment. The UV printing was created from scanned digital images of a real wood surface and processed in graphics software. The images were converted to grayscale graphics by selecting color range and setting the parameter of fuzziness. The grayscale images were printed as 3D texture height simulation on the substrates and coated by printing the color images as texture mapping. Based on these wood texture digital images, the marquetry art is also considered in the images processing design to increase the artistry of the printed materials. The medium-density fiberboard (MDF) coated printing marquetry surface replicate realistic natural 3D wood texture surface layers on wood-based panels and imitated the effect of handcrafted wood art works. This study proves that printing 3D texture surface material is creative and valuable with ecologically friendly, low-consumption UV-curable inkjet technology and provides a feasible and scalable approach in flooring/furniture/decorative architectural panels. Full article

Nondestructive inspection (NDI) has immensely contributed to the restoration of historic and artistic works. As one of the most common used NDI methods, active thermography is an easy-to-operate and efficient technique. Principal component thermography (PCT) has been widely used to deal with thermographic data for enhancing the visibility of subsurface defects. Unlike PCT, edge-group sparse PCT introduced herein enforces sparsity of principal component (PC) loadings by considering the spatial connectivity of thermographic image pixels. The feasibility and effectiveness of this method is illustrated by the experimental results of the defect characterization in an ancient marquetry sample with a fir wood support. Full article

The process of using fungal-colored wood (spalted wood) for marquetry and intarsia woodworks in Italy and Germany was very popular from the 1400–1600s, with some instances continuing as late as the 1800s. While spalted marquetry in these countries is relatively well documented, less is known about its use in other parts of Europe. One of the primary reasons for this lack of knowledge is the difficulty in identifying spalted wood, especially the blue-green variety produced from Chlorociboria species, from wood dyed with copper-based compounds or other synthetics. The most reliable testing method involves destructive sampling, where a small piece is taken from the work, the pigment extracted, and an analysis performed. Such sampling is simply not feasible, nor often allowed, on ancient artwork. To make a reliable, non-destructive identification of spalted wood, a visual method based on anatomical characteristics of spalted wood was developed to differentiate spalted wood from dyed wood. High-resolution pictures were taken from wooden artifacts containing blue-green colored wood in collections at the National Museum of Decorative Arts (MNAD), the Royal Site Monastery El Escorial and the Bilbao Museum of Fine Arts in Spain. The concentration of pigment in the rays, the color distribution, the size of the piece and the date of production, were analyzed. With the use of this new visual method, it was possible to determine that intarsia artifacts, held in Spain but of Augsburg origin from the 1500–1600s, contained spalted wood details. Meanwhile, Spanish and Italian intarsia artifacts from the 1800s were found to only contain dyed wood. Full article