Rock characterization is typically performed by geologists in mining companies and involves the analysis of several meters of drill-hole samples to describe distinctive geological properties. In this procedure, rock texture is not typically taken into account despite its importance given its close relation with metallurgical responses and, therefore, all mineral processes. To support the work of geology experts, this research seeks to obtain rock texture information, discriminating it from digital images through image processing and machine learning techniques. For this purpose, a geologist-labeled digital photograph database was used with different rock texture classes (including geological textures and structures) from drill-hole samples. To characterize rock texture, three texture descriptors based on variographic information are proposed, which summarize data contained in the image pixels, focusing on local structural patterns that numerically describe its texture properties. Then, based on a methodology of image texture comparison, which could be extended to classify different types of rock texture classes, a quantification of the system’s performance was obtained. The results showed a high discrimination among common texture classes using compact variogram-based features that outperformed previous methods applied on the same rock texture database.

中文翻译：

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基于变异函数的描述符对岩石纹理图像进行比较和分类

岩石定性通常由采矿公司的地质学家进行，涉及对几米钻孔样品的分析，以描述独特的地质特性。在此过程中，尽管岩石的质地非常重要，但考虑到它与冶金反应以及所有矿物过程之间的密切关系，尽管岩石的重要性很重要。为了支持地质专家的工作，本研究旨在获取岩石纹理信息，并通过图像处理和机器学习技术将其与数字图像区分开。为此，使用了地质学家标记的数字照片数据库，该数据库具有钻孔样品中不同的岩石纹理类别（包括地质纹理和结构）。为了表征岩石的质地，提出了三种基于变异信息的质地描述子，其中总结了图像像素中包含的数据，着重于用数字描述其纹理特性的局部结构图案。然后，基于图像纹理比较的方法，该方法可以扩展为对不同类型的岩石纹理类别进行分类，从而获得了系统性能的量化结果。结果表明，使用紧凑的基于变异函数的特征可对常见的纹理类别进行高度区分，其性能优于应用于相同岩石纹理数据库的先前方法。