Artificial Intelligence Applied to the Characterization and Comparison of Fabrics of Canvases in Art Masterpieces

ABSTRACT

This project addresses the application of signal processing and artificial intelligence to the analysis of x-rays of paintings from the 16th-18th centuries. The objective is to develop methods and algorithms that are useful when it comes to simplifying and automating the work of painting conservators of this period, so that they are reliable, repeatable and can be applied in a standardized way. The proposal focuses, on the one hand, on the analysis of a work to extract descriptors that characterize it. Although the analysis of simple fabrics such as taffetas has been extensively studied, in certain works, such as a significant part of the Velázquez collection, current methods fail. Here it is proposed as a novelty to analyze these fabrics using deep learning. In addition, more complex twill fabrics by painters such as Murillo or El Greco have rarely been studied, and they will be characterized within the framework of this project. On the other hand, the main objective of this proposal is to be able to compare the degree of similarity between canvases, which allows us to conclude if a set of works were painted by the same author, in the same period or in the same place. The analysis of requirements is carried out in collaboration with the Museo Nacional del Prado, as well as the evaluation of the results.

GOALS

  • Study complex twill fabrics to help curators in the analysis of these fabrics
  • Improve Aracne, a thread counting algorithm based on frequency analysis, to make it freely available.
  • Apply deep learning to improve the frequency analysis, in particular to analyze the collection by Velázquez at El Museo Nacional del Prado.
  • Explore new ways of finding matches between fabrics of canvases not based on the correspondence of the thread counting maps.

FUNDING AND COLLABORATORS

This project was funded by Consejería de Economía y Conocimiento, Junta de Andaluc ía and European Union in the framework of the Program FEDER Andalucía 2014-2010 “Crecimiento inteligente: una economía basada en el conocimiento y la innovación” for funding this research under the ATENEA Project P20-01216.

El Museo Nacional del Prado has been a relevant partner of this project, providing the needed material to perform the investigation. We also thank The National Gallery and especially Catherine Higgitt for providing the canvases by Poussin to allow for comparison with SOTA approaches.

PUBLICATIONS

Journals

  1. A. Delgado, J. J. Murillo-Fuentes, L. Alba-Carcelén  (2023), “Thread Counting in Plain Weave for Old Paintings Using Semi-Supervised Regression Deep Learning Models” Submitted to Neural Networks, see paper.
  2. A. Delgado, L. Alba-Carcelén, J. J. Murillo-Fuentes (2023), “Crossing Points Detection in Plain Weave for Old Paintings with Deep Learning,” submitted to Engineering Application of Artificial Intelligence, see paper.

Congress

  1. María del Mar Velasco Montero, Juan José Murillo-Fuentes, Laura Alba-Carcelén (2022), “Complex Twill Fabrics Pattern Recognition in Canvases”, in Computational approaches for technical imaging in cultural heritage (7th IP4AI meeting). April, London. https://art-ict.github.io/artict/Conference.html
  2. Antonio D. Bejarano, Juan José Murillo-Fuentes, Laura Alba-Carcelén (2022), “Crossings Segmentation in Plain Weaves for X-Rays of Canvases with Deep Learning”, Computational approaches for technical imaging in cultural heritage (7th IP4AI meeting). April, London. https://art-ict.github.io/artict/Conference.html
  3. Antonio D. Bejarano, Juan José Murillo-Fuentes, Laura Alba-Carcelén (2022), “Crossings Segmentation in Plain Weaves for X-Rays of Canvases with Deep Learning”, Computational approaches for technical imaging in cultural heritage (7th IP4AI meeting). April, London. https://art-ict.github.io/artict/Conference.html