Astronomical wide‐field imaging performed with new large‐format CCD detectors poses data reduction problems of unprecedented scale, which are difficult to deal with using traditional interactive tools. We present here NExt (Neural Extractor), a new neural network (NN) based package capable of detecting objects and performing both deblending and star/galaxy classification in an automatic way. Traditionally, in astronomical images, objects are first distinguished from the noisy background by searching for sets of connected pixels having brightnesses above a given threshold; they are then classified as stars or as galaxies through diagnostic diagrams having variables chosen according to the astronomer's taste and experience. In the extraction step, assuming that images are well sampled, NExt requires only the simplest a priori definition of ‘what an object is’ (i.e. it keeps all structures composed of more than one pixel) and performs the detection via an unsupervised NN, approaching detection as a clustering problem that has been thoroughly studied in the artificial intelligence literature. The first part of the NExt procedure consists of an optimal compression of the redundant information contained in the pixels via a mapping from pixel intensities to a subspace individualized through principal component analysis. At magnitudes fainter than the completeness limit, stars are usually almost indistinguishable from galaxies, and therefore the parameters characterizing the two classes do not lie in disconnected subspaces, thus preventing the use of unsupervised methods. We therefore adopted a supervised NN (i.e. a NN that first finds the rules to classify objects from examples and then applies them to the whole data set). In practice, each object is classified depending on its membership of the regions mapping the input feature space in the training set. In order to obtain an objective and reliable classification, instead of using an arbitrarily defined set of features we use a NN to select the most significant features among the large number of measured ones, and then we use these selected features to perform the classification task. In order to optimize the performance of the system, we implemented and tested several different models of NN. The comparison of the NExt performance with that of the best detection and classification package known to the authors (SExtractor) shows that NExt is at least as effective as the best traditional packages.

A lower semicontinuity result is proved in the space of special vector fields with bounded deformation for a fracture energetic model of the typeA representation of the energy density Ψ , which ensures lower semicontinuity, is also given.

An inlay sample with artificial defects was inspected via the pulse-compression thermography (PuCT) technique. The sample belongs to the cultural heritage field, and it was realized by a professional restorer based on his long-time experience, imitating historical art crafting styles. The tesserae composing the inlay were not treated by any protective paints, so that external thermal stimuli may cause physical/mechanical alterations of the cell walls, with consequent colour changes, cracks, and eventually damage to its surface. To avoid any alteration of the sample, the PuCT technique was used for inspecting the inlay sample as it allows the heating power to be very low, while assuring enough thermal contrast for the defects to be detected after the exploitation of the pulse-compression algorithm. Even if a maximum ΔT slightly exceeding 1 °C was detected during the PuCT test of the inlay sample, it is shown that this is enough for detecting several defects. Further, image processing based on the Hilbert transform increases defect detection and characterization. In addition, a novel normalization technique, i.e., a pixel-by-pixel data normalization with respect to the absorbance estimated by considering a characteristic value of the compression peak, is introduced here for the first time. The proposed normalization enhances the defect detection capability with respect to the standard pixel-by-pixel amplitude visualization. This has been demonstrated for two experimental setups, both exploiting the same LED chips system as heating source but different thermal camera sensors, i.e., one in the mid-infrared spectrum, the other in the far infrared one. Thus, the present work is also the first small-scale test of a future portable system that will include low-power LED chip feed in DC by metal-oxide-semiconductor field-effect transistor (MOSFET) devices, and a handy far-infrared camera.

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.

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Marquetry method is important in the culture of the Italian community as can be witnessed from the large quantity of artworks that have been realized in this way. The monitoring of the integrity of such pieces poses a great challenge given the need of a reliable and nondestructive technique able to detect surface and subsurface defects. In this work, two ancient marquetry samples containing natural defects were inspected thanks to active thermography by using time-tested, safe and resilient advanced signal processing algorithms (i.e., principal component thermography, correlation contrast, pulsed phase Fourier transform amplitude and phase, cold image subtraction contrast, and polynomial fitting). The latter have been applied to provide a 2D map of the defects.Anyway, in the cultural heritage field, one of the main interest of restorers is the volume of the subsurface defects for structural analyses. The emphasis in this study is placed on the use of dynamic thermal tomography (DTT) as an advanced technique of active thermal non-destructive testing. The main concepts of DTT are illustrated in the manuscript, while a special technique for defect thermal characterization has been used during the second analysis to validate tomographic results. Finally, the position of the main defects retrieved by means of the established techniques applied during the first analysis have been confirmed by DTT.

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