dc.creator | Delgado Bejarano, Antonio | es |
dc.creator | Alba-Carcelén, Laura | es |
dc.creator | Murillo Fuentes, Juan José | es |
dc.date.accessioned | 2023-11-09T11:08:43Z | |
dc.date.available | 2023-11-09T11:08:43Z | |
dc.date.issued | 2023-11 | |
dc.identifier.citation | Delgado Bejarano, A., Alba-Carcelén, L. y Murillo Fuentes, J.J. (2023). Crossing points detection in plain weave for old paintings with deep learning. Engineering Applications of Artificial Intelligence, 126 (107100), 1-12. https://doi.org/10.1016/j.engappai.2023.107100. | |
dc.identifier.issn | 0952-1976 | es |
dc.identifier.uri | https://hdl.handle.net/11441/150378 | |
dc.description | This is an open access article under the CC BY-NC-ND license | es |
dc.description.abstract | In the forensic studies of painting masterpieces, the analysis of the support is of major importance. For plain
weave fabrics, the densities of vertical and horizontal threads are used as main features, while angle deviations
from the vertical and horizontal axis are also of help. These features can be studied locally through the canvas.
In this work, deep learning is proposed as a tool to perform these local densities and angle studies. We trained
the model with samples from 36 paintings by Velázquez, Rubens or Ribera, among others. The data preparation
and augmentation are dealt with at a first stage of the pipeline. We then focus on the supervised segmentation
of crossing points between threads. The U-Net with inception and Dice loss are presented as good choices
for this task. Densities and angles are then estimated based on the segmented crossing points. We report test
results of the analysis of a few canvases and a comparison with methods in the frequency domain, widely used
in this problem. We concluded that this new approach successes in some cases where the frequency analysis
tools fail, while improves the results in others. Besides, our proposal does not need the labeling of part of the
to be processed image. As case studies, we apply this novel algorithm to the analysis of two pairs of canvases
by Velázquez and Murillo, to conclude that the fabrics used came from the same roll. | es |
dc.description.sponsorship | Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Junta de Andalucía y la Unión Europea P20_01216 PID2021-123182OB-I00 216 PID2021-127871OB-I00 | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación de España MCIN/AEI/10.13039/501100011033 | es |
dc.format | application/pdf | es |
dc.format.extent | 12 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Engineering Applications of Artificial Intelligence, 126 (107100), 1-12. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | X-ray processing | es |
dc.subject | Canvas weave thread counting | es |
dc.subject | Image segmentation | es |
dc.subject | Deep learning | es |
dc.subject | U-Net | es |
dc.title | Crossing points detection in plain weave for old paintings with deep learning | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones | es |
dc.relation.projectID | P20_01216 | es |
dc.relation.projectID | PID2021-123182OB-I00 | es |
dc.relation.projectID | PID2021-127871OB-I00] | es |
dc.relation.projectID | MCIN/AEI/10.13039/501100011033 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0952197623012848?via%3Dihub | es |
dc.identifier.doi | 10.1016/j.engappai.2023.107100 | es |
dc.contributor.group | Universidad de Sevilla. TIC-155:Tratamiento de señales y comunicaciones. | es |
dc.journaltitle | Engineering Applications of Artificial Intelligence | es |
dc.publication.volumen | 126 | es |
dc.publication.issue | 107100 | es |
dc.publication.initialPage | 1 | es |
dc.publication.endPage | 12 | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Junta de Andalucía | es |
dc.contributor.funder | European Union | es |