Delgado Bejarano, AntonioMurillo Fuentes, Juan JoséCarcelén-Alba, Laura2025-07-212025-07-212025Delgado Bejarano, A., Murillo Fuentes, J.J. y Carcelén-Alba, L. (2025). Thread Counting in Plain Weave for Old Paintings Using Regression Deep Learning Models. International Journal of Computer Vision. https://doi.org/https://doi.org/10.1007/s11263-025-02473-9.0920-56911573-1405https://hdl.handle.net/11441/175484This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.In this paper, we introduce a novel algorithm designed to improve thread density estimation in canvas analysis. Our approach incorporates three major contributions. First, we eliminate the need for post-segmentation processing by integrating regression techniques, enabling the deep learning (DL) model to directly compute thread density. This does not only reduce computational time but also shifts the training focus from locating crossing points to minimizing thread counting errors, thereby enhancing accuracy. We develop and rigorously evaluate various models, selecting the one with optimal performance through a hyperparameter search. Second, we refine the data generation process by dynamically adjusting filter lengths based on initial thread density estimates and incorporating equalization. We also enhance data augmentation. Third, we implement semi-supervised training to expand the dataset and fine-tune model weights. This involves incorporating new inputs into the training set when both the DL model and Fourier transform yield similar density estimates for new paintings. Our proposed algorithm demonstrates superior performance in thread density error reduction and operational efficiency compared to previous DL segmentation solutions for masterpieces from Ribera, Velázquez, or Poussin. Additionally, it has been effectively applied to identify fabric matches between canvases attributed to different authors, showcasing its practical applicability in art analysis.application/pdf16 p.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/X-ray image processingCanvas weaveThread CountingDeep LearningRegressionInceptioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1007/s11263-025-02473-9