Abstract In this paper the estimation of masonry characteristics by means of thermographic images, enhanced by sampling Kantorovich algorithm, is taken into account. In particular, the convergence of the Statistical Volume Element (SVE) to the Representative Volume Element (RVE) is analyzed. It is found that the enhancement, obtained by the proposed procedure, allows a faster convergence of SVE to RVE and a reduced coefficient of variation of the estimates obtained using a partition of the image with windows of smaller dimensions. Moreover, the effects of the uncertainties in the parameters involved in the reconstruction of texture from thermographic image, such as those used in morphological operators employed in digital image processing, involving the environmental conditions of the samples and the properties of the kernel utilized in the image enhancement algorithm, have been studied in order to assess their influence on the estimated mechanical characteristics. The performed analyses contribute to increase the reliability of the thermography as tool for identifying of masonries covered with plaster or frescoes, which is a very frequent case in vulnerability analysis of historical buildings.

中文翻译：

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通过应用于热成像数字图像的采样算法提高砌体特性估计的可靠性

摘要 本文考虑了通过采样Kantorovich 算法增强的热成像图像对砌体特性的估计。特别地，分析了统计体积元素 (SVE) 与代表性体积元素 (RVE) 的收敛性。发现通过所提出的程序获得的增强允许 SVE 更快地收敛到 RVE 并降低使用具有较小尺寸窗口的图像分区获得的估计的变异系数。此外，从热成像图像重建纹理所涉及的参数不确定性的影响，例如在数字图像处理中使用的形态学算子中使用的参数，已经研究了涉及样品的环境条件和图像增强算法中使用的内核的特性，以评估它们对估计的机械特性的影响。所进行的分析有助于提高热成像作为识别覆盖有灰泥或壁画的砖石的工具的可靠性，这是历史建筑脆弱性分析中非常常见的情况。