Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the Ghent Altarpiece.
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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.
Published Version (Please cite this version)
Sabetsarvestani, Z, B Sober, C Higgitt, I Daubechies and MRD Rodrigues (2019). Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the Ghent Altarpiece. Science advances, 5(8). p. eaaw7416. 10.1126/sciadv.aaw7416 Retrieved from https://hdl.handle.net/10161/19564.
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I am currently privilaged to be working with Prof. Ingrid Daubechies. Before that, I have completed my PhD in applied mathematics at Tel-Aviv University under the mentoring of Prof. David Levin. My MSc was co-mentored by Prof. Levin and Prof. Israel Finkelstein from the Department of Archaeology and Ancient Near Eastern Civilizations.
My research ranges between analysis of high dimensional data from a geometrical perspective and the application of mathematical and statistical methods in digital humanities.
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