Craquelure as a Graph: Application of Image Processing and Graph Neural Networks to the Description of Fracture Patterns

Cracks on a painting is not a defect but an inimitable signature of an artwork which can be used for origin examination, aging monitoring, damage identification, and even forgery detection. This work presents the development of a new methodology and corresponding toolbox for the extraction and characterization of information from an image of a craquelure pattern. The proposed approach processes craquelure network as a graph. The graph representation captures the network structure via mutual organization of junctions and fractures. Furthermore, it is invariant to any geometrical distortions. At the same time, our tool extracts the properties of each node and edge individually, which allows to characterize the pattern statistically. We illustrate benefits from the graph representation and statistical features individually using novel Graph Neural Network and hand-crafted descriptors correspondingly. However, we also show that the best performance is achieved when both techniques are merged into one framework. We perform experiments on the dataset for paintings' origin classification and demonstrate that our approach outperforms existing techniques by a large margin.Comment: Published in ICCV 2019 Workshop

Assisting classical paintings restoration : efficient paint loss detection and descriptor-based inpainting using shared pretraining

In the restoration process of classical paintings, one of the tasks is to map paint loss for documentation and analysing purposes. Because this is such a sizable and tedious job automatic techniques are highly on demand. The currently available tools allow only rough mapping of the paint loss areas while still requiring considerable manual work. We develop here a learning method for paint loss detection that makes use of multimodal image acquisitions and we apply it within the current restoration of the Ghent Altarpiece. Our neural network architecture is inspired by a multiscale convolutional neural network known as U-Net. In our proposed model, the downsampling of the pooling layers is omitted to enforce translation invariance and the convolutional layers are replaced with dilated convolutions. The dilated convolutions lead to denser computations and improved classification accuracy. Moreover, the proposed method is designed such to make use of multimodal data, which are nowadays routinely acquired during the restoration of master paintings, and which allow more accurate detection of features of interest, including paint losses. Our focus is on developing a robust approach with minimal user-interference. Adequate transfer learning is here crucial in order to extend the applicability of pre-trained models to the paintings that were not included in the training set, with only modest additional re-training. We introduce a pre-training strategy based on a multimodal, convolutional autoencoder and we fine-tune the model when applying it to other paintings. We evaluate the results by comparing the detected paint loss maps to manual expert annotations and also by running virtual inpainting based on the detected paint losses and comparing the virtually inpainted results with the actual physical restorations. The results indicate clearly the efficacy of the proposed method and its potential to assist in the art conservation and restoration processes

Virtual restoration of the Ghent altarpiece using crack detection and inpainting

In this paper, we present a new method for virtual restoration of digitized paintings, with the special focus on the Ghent Altarpiece (1432), one of Belgium's greatest masterpieces. The goal of the work is to remove cracks from the digitized painting thereby approximating how the painting looked like before ageing for nearly 600 years and aiding art historical and palaeographical analysis. For crack detection, we employ a multiscale morphological approach, which can cope with greatly varying thickness of the cracks as well as with their varying intensities (from dark to the light ones). Due to the content of the painting (with extremely many fine details) and complex type of cracks (including inconsistent whitish clouds around them), the available inpainting methods do not provide satisfactory results on many parts of the painting. We show that patch-based methods outperform pixel-based ones, but leaving still much room for improvements in this application. We propose a new method for candidate patch selection, which can be combined with different patch-based inpainting methods to improve their performance in crack removal. The results demonstrate improved performance, with less artefacts and better preserved fine details

An Integrated Content and Metadata based Retrieval System for Art

In this paper we describe aspects of the Artiste project to develop a distributed content and metadata based analysis, retrieval and navigation system for a number of major European Museums. In particular, after a brief overview of the complete system, we describe the design and evaluation of some of the image analysis algorithms developed to meet the specific requirements of the users from the museums. These include a method for retrievals based on sub images, retrievals based on very low quality images and retrieval using craquelure type

Digital image processing of the Ghent altarpiece : supporting the painting's study and conservation treatment

In this article, we show progress in certain image processing techniques that can support the physical restoration of the painting, its art-historical analysis, or both. We show how analysis of the crack patterns could indicate possible areas of overpaint, which may be of great value for the physical restoration campaign, after further validation. Next, we explore how digital image inpainting can serve as a simulation for the restoration of paint losses. Finally, we explore how the statistical analysis of the relatively simple and frequently recurring objects (such as pearls in this masterpiece) may characterize the consistency of the painter’s style and thereby aid both art-historical interpretation and physical restoration campaign

Multi-modal dictionary learning for image separation with application in art investigation

In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals to be separated have similar morphological characteristics, which brings previous source separation methods to their limits. Our solution is to use photographs taken from the front and back-side of the panel to drive the separation process. The crux of our approach relies on the coupling of the two imaging modalities (photographs and X-rays) using a novel coupled dictionary learning framework able to capture both common and disparate features across the modalities using parsimonious representations; the common component models features shared by the multi-modal images, whereas the innovation component captures modality-specific information. As such, our model enables the formulation of appropriately regularized convex optimization procedures that lead to the accurate separation of the X-rays. Our dictionary learning framework can be tailored both to a single- and a multi-scale framework, with the latter leading to a significant performance improvement. Moreover, to improve further on the visual quality of the separated images, we propose to train coupled dictionaries that ignore certain parts of the painting corresponding to craquelure. Experimentation on synthetic and real data - taken from digital acquisition of the Ghent Altarpiece (1432) - confirms the superiority of our method against the state-of-the-art morphological component analysis technique that uses either fixed or trained dictionaries to perform image separation.Comment: submitted to IEEE Transactions on Images Processin

Crack patterns over uneven substrates

Cracks in thin layers are influenced by what lies beneath them. From buried craters to crocodile skin, crack patterns are found over an enormous range of length scales. Regardless of absolute size, their substrates can dramatically influence how cracks form, guiding them in some cases, or shielding regions from them in others. Here we investigate how a substrate’s shape affects the appearance of cracks above it, by preparing mud cracks over sinusoidally varying surfaces. We find that as the thickness of the cracking layer increases, the observed crack patterns change from wavy to ladder-like to isotropic. Two order parameters are introduced to measure the relative alignment of these crack networks, and, along with Fourier methods, are used to characterise the transitions between crack pattern types. Finally, we explain these results with a model, based on the Griffith criteria of fracture, that identifies the conditions for which straight or wavy cracks will be seen, and predicts how well-ordered the cracks will be. Our metrics and results can be applied to any situation where connected networks of cracks are expected, or found

Decouverte d'un brachiopode inarticulé <i>Acrothele</i> cf. <i>Bergeroni</i> Walcott, dans le Revinien inferieur de trois-ponts, Cambrien du Massif de Stavelot, Belgique

The Revinian (Cambrian) of the Ardenne, from which no macrofossils have hitherto been recorded, has yielded two inarticulate brachiopods determined here as Acrothele cf. bergeroni WALCOTT. The specimens were found south of Trois-Ponts in the Stavelot Massif, at a horizon referred to division Rn1a. The stratigraphic age suggested by the brachiopods is compared with that indicated by acritarchs : a Middle Cambrian age seems probable

Préparation et propriétés diélectriques du Ba0,90Sr0,10TiO3 dopé au manganèse

National audienceDans le cadre de cette étude, des couches minces de Ba0,90Sr0,10TiO3 dopées au manganèse ont été réalisées par un procédé sol-gel modifié basé sur des précurseurs alkoxydes. La cristallinité et la morphologie des films ont été étudiées montrant que le manganèse ne modifie pas significativement les propriétés structurales du matériau. Les cycles d'hystérésis à 50 Hz ont été mesurés et un cycle saturé présentant les meilleures propriétés a été obtenu pour un dopage à 3 %. La permittivité et les pertes diélectriques (tan ) sont mesurées à 1 MHz en fonction d'un champ continu permettant ainsi d'estimer l'accordabilité et la figure de mérite (F.O.M.) de chaque échantillon. Un dopage au manganèse de 3 % molaire semble finalement offrir le meilleur compromis entre accordabilité et pertes diélectriques

Potassium, calcium et magnésium dans la nutrition de l'ananas en Guinée. II. Influence sur le rendement commercialisable

Les résultats agronomiques de l'essai dont le plan et le déroulement ont été décrits précédemment (Fruits, vol. 16, n¼ 2, p. 49) sont exposés. On étudie tour à tour les pourcentages de fructification naturelle et provoquée, le poids moyen des fruits, le tonnage récolté par hectare, les causes possibles de refus ou de dépréciation, et la production de rejets. Ces données convergent vers la recommandation d'une fertilisation différant des formules usuelles d'engrais par la suppression quasi totale de la chaux, l'augmentation de la potasse et, fait essentiel, l'introduction d'une dose de magnésie d'autant plus considérable que l'on craint davantage la sécheresse et l'insuccès des traitements précoces de floraison. Etant donnée la nouveauté de ces types de fumure, il n'est pas conseillé de les adopter dans la pratique avant de connaître le résultat d'un essai complémentaire, installé en 1960 pour en vérifier l'utilit