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<p>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.</p>

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Thread Counting in Plain Weave for Old Paintings Using Regression Deep Learning Models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月2日

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  • Antonio Delgado,
  • Juan José Murillo-Fuentes,
  • Laura Alba-Carcelén

摘要

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.