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

Loading...
Thumbnail Image

Date

2019-08-30

Journal Title

Journal ISSN

Volume Title

Repository Usage Stats

178
views
55
downloads

Citation Stats

Attention Stats

Abstract

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.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1126/sciadv.aaw7416

Publication Info

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.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Daubechies

Ingrid Daubechies

James B. Duke Distinguished Professor of Mathematics and Electrical and Computer Engineering

Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.