Artículo
Crossing points detection in plain weave for old paintings with deep learning
Autor/es | Delgado Bejarano, Antonio
Alba-Carcelén, Laura Murillo Fuentes, Juan José |
Departamento | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones |
Fecha de publicación | 2023-11 |
Fecha de depósito | 2023-11-09 |
Publicado en |
|
Resumen | 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 ... 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. |
Agencias financiadoras | Ministerio de Ciencia e Innovación (MICIN). España Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Junta de Andalucía European Union |
Identificador del proyecto | P20_01216
PID2021-123182OB-I00 PID2021-127871OB-I00] MCIN/AEI/10.13039/501100011033 |
Cita | 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. |
Ficheros | Tamaño | Formato | Ver | Descripción |
---|---|---|---|---|
EAAI_2023_Delgado_Crossing_OA.pdf | 4.080Mb | [PDF] | Ver/ | |