Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the Ghent Altarpiece

X-ray images of polyptych wings, or other artworks painted on both sides of their support, contain in one image content from both paintings, making them difficult for experts to “read.” To improve the utility of these x-ray images in studying these artworks, it is desirable to separate the content into two images, each pertaining to only one side. This is a difficult task for which previous approaches have been only partially successful. Deep neural network algorithms have recently achieved remarkable progress in a wide range of image analysis and other challenging tasks. We, therefore, propose a new self-supervised approach to this x-ray separation, leveraging an available convolutional neural network architecture; results obtained for details from the Adam and Eve panels of the Ghent Altarpiece spectacularly improve on previous attempts

Source Separation in the Presence of Side-information

The source separation problem involves the separation of unknown signals from their mixture. This problem is relevant in a wide range of applications from audio signal processing, communication, biomedical signal processing and art investigation to name a few. There is a vast literature on this problem which is based on either making strong assumption on the source signals or availability of additional data. This thesis proposes new algorithms for source separation with side information where one observes the linear superposition of two source signals plus two additional signals that are correlated with the mixed ones. The first algorithm is based on two ingredients: first, we learn a Gaussian mixture model (GMM) for the joint distribution of a source signal and the corresponding correlated side information signal; second, we separate the signals using standard computationally efficient conditional mean estimators. This also puts forth new recovery guarantees for this source separation algorithm. In particular, under the assumption that the signals can be perfectly described by a GMM model, we characterize necessary and sufficient conditions for reliable source separation in the asymptotic regime of low-noise as a function of the geometry of the underlying signals and their interaction. It is shown that if the subspaces spanned by the innovation components of the source signals with respect to the side information signals have zero intersection, provided that we observe a certain number of linear measurements from the mixture, then we can reliably separate the sources; otherwise we cannot. The second algorithms is based on deep learning where we introduce a novel self-supervised algorithm for the source separation problem. Source separation is intrinsically unsupervised and the lack of training data makes it a difficult task for artificial intelligence to solve. The proposed framework takes advantage of the available data and delivers near perfect separation results in real data scenarios. Our proposed frameworks – which provide new ways to incorporate side information to aid the solution of the source separation problem – are also employed in a real-world art investigation application involving the separation of mixtures of X-Ray images. The simulation results showcase the superiority of our algorithm against other state-of-the-art algorithms

X-ray fluorescence spectroscopic technique for quality control in pharmaceutical, agronomy and automobile industry from a biomedical perspective

In this work, we investigate the versatility of X-ray fluorescence spectroscopic technique specifically in biomedical applications. To prove the robustness of the method we devel- oped three case studies from three very distinct industries with different backgrounds. In the first example, we study the application to the pharmaceutical industry by analyz- ing a group of samples of commercially available iron supplement pills. This analysis shows that even though iron concentrations are measured to meet the advertised values, there are important elemental contaminants, specifically, manganese and nickel. In the second example, we have analyzed food samples, more specifically, biofortified wheat grains. This analysis focus on assessing the ability of different genotypes for retaining the biofortified element - zinc. Even though most sample show enhanced zinc content, zinc distribution inside the grain is not homogeneous and this element tends to be found in higher concentrations in the embryo and vascular bundle. In the third example, we have developed a study in collaboration with the biggest automotive plant in Portugal – Volkswagen Autoeuropa. The main goal is to install, at least, one low-cost air quality control device in one of the workstations of the factory. For this purpose, we designed two prototypes that used the wind tunnel principle to deposit suspended particles in these workstations onto quartz fiber filters. These filters were then analyzed not only by XRF but also via microscopic camera analysis, SEM with EDX. Results for both size and com- position concentrations show a linear behavior with deposition time, for a range of four identified trace elements: Iron, Zinc, Manganese and Copper. From these samples we conclude that the number of suspended metallic particles is not negligible and a repeated and accumulated long time exposure might have health consequences.Neste trabalho foi estudada a versatilidade da técnica de espectroscopia de fluorescência de Raios-X especificamente em aplicações biomédicas. Para demonstrar a robustez do método foram investigados três cenários provenientes de industrias de origens e nature- zas muito distintas. No primeiro exemplo discutido, estudou-se a aplicação à industria farmacêutica, aplicando a técnica a um conjunto de amostras de suplementos de ferro comercialmente disponíveis. Esta análise demonstrou que apesar das concentrações de ferro estarem de acordo com as tabeladas no rótulo, existem contaminantes importantes, nomeadamente níquel e manganês. No segundo exemplo, foram analisadas amostras de comida, especificamente, trigo biofortificado. Esta análise focou-se num conjunto grande de amostras onde se averiguou a habilidade de reter o elemento biofortificado - zinco - em cada um dos genótipos de trigo analisados. Apesar dos grãos biofortificados mostrarem um claro aumento de zinco, a distribuição de zinco no grão não é no entanto uniforme, sendo que, na grande maioria dos casos, este elemento está concentrado no embrião e sistema vascular. O terceiro exemplo, foi desenvolvido em ambiente industrial na maior fábrica automóvel de Portugal - Volkswagen Autoeuropa. O foco principal deste trabalho é o desenvolvimento e instalação de uma solução low-cost de controlo de qualidade do ar num dos postos de trabalho na linha de montagem da fábrica. Para tal, desenvolvemos dois protótipos baseados num conceito clássico de túnel de vento para possibilitar a depo- sição das partículas suspensas no ar num filtro de fibra de quartzo. Para além da análise por XRF estes filtros foram também sujeitos a inspecção óptica por micro-camera binocu- lar e análise SEM com EDX. Os resultados mostram que existe um relação linear entre o tempo de deposição e a concentração de partículas nos filtros e revelam que os elementos de traço desta análise são o ferro, o zinco, o manganês e o cobre. Esta análise revela que temos quantidades de todos estes elementos acima dos nossos limites de detecção e como tal o impacto na saúde dos funcionários devido a uma exposição de longa duração deve ser apurado