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, Vel\'azquez, 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.
翻译:在本研究中,作者们开发了基于深度学习的回归方法,用于实现平纹画布分析中的线密度估计。先前的方法基于傅里叶分析,对于某些场景非常鲁棒,但在其他一些场景中失败,在机器学习工具中,涉及到对所涉及的绘画进行预标记或交叉点分割,该方法在所有场景中都提供了良好的估计,不需要预标记。交叉点分割方法耗时,因为根据位置确定交叉点后才进行密度估计。在这个新颖的提议中,我们通过使用回归深度学习模型直接从图像计算线密度来避免此步骤。我们还引入了一些改进的初步预处理,对最终误差产生影响。提出了几个模型并进行分析以保留最佳模型。此外,我们还通过引入半监督方法进一步降低了密度估计误差。我们分析了我们的新算法在 Ribera、Vel\'azquez 和 Poussin 的作品中的性能,其中我们将我们的结果与以前的方法进行了比较。最后,该方法被用于支持普拉多博物馆大师作品的作者更换。