Thread Counting in Plain Weave for Old Paintings Using Semi-Supervised Regression Deep Learning Models
This work addresses the problem of accurately and efficiently analyzing canvas weave patterns for art authentication, particularly in museums like the Museo del Prado, though it is incremental as it builds on existing segmentation approaches.
The authors tackled thread density estimation in plain weave canvas analysis by developing a regression deep learning model that directly computes densities from images, avoiding time-consuming segmentation steps, and achieved reduced error rates compared to previous Fourier and machine learning methods in tests on paintings by artists like Ribera and Velázquez.
In this work, the authors develop regression approaches based on deep learning to perform thread density estimation for plain weave canvas analysis. Previous approaches were based on Fourier analysis, which is quite robust for some scenarios but fails in some others, in machine learning tools, that involve pre-labeling of the painting at hand, or the segmentation of thread crossing points, that provides good estimations in all scenarios with no need of pre-labeling. The segmentation approach is time-consuming as the estimation of the densities is performed after locating the crossing points. In this novel proposal, we avoid this step by computing the density of threads directly from the image with a regression deep learning model. We also incorporate some improvements in the initial preprocessing of the input image with an impact on the final error. Several models are proposed and analyzed to retain the best one. Furthermore, we further reduce the density estimation error by introducing a semi-supervised approach. The performance of our novel algorithm is analyzed with works by Ribera, Velázquez, and Poussin where we compare our results to the ones of previous approaches. Finally, the method is put into practice to support the change of authorship or a masterpiece at the Museo del Prado.