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A new computational approach to cracks quantification from 2D image analysis: Application to micro-cracks description in rocks

机译:一种从二维图像分析中量化裂纹的新计算方法:在岩石微裂纹描述中的应用

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摘要

In this paper we propose a crack quantification method based on 2D image analysis. This technique is applied to a gray level Scanning Electron Microscope (SEM) images, segmented and converted in Black and White (B/W) images using the Trainable Segmentation plugin of Fiji. Resulting images are processed using a novel Matlab script composed of three different algorithms: the separation algorithm, the filtering and quantification algorithm and the orientation one. Initially the input image is enhanced via 5 morphological processes. The resulting lattice is "cut" into single cracks using 1 pixel-wide bisector lines originated from every node. Cracks are labeled using the connected-component method, then the script computes geometrical parameters, such as width, length, area, aspect ratio and orientation. A filtering is performed using a user-defined value of aspect ratio, followed by a statistical analysis of remaining cracks. In the last part of this paper we discuss about the efficiency of this script, introducing an example of analysis of two datasets with different dimension and resolution; these analyses are performed using a notebook and a high-end professional desktop solution, in order to simulate different working environments.
机译:在本文中,我们提出了一种基于二维图像分析的裂纹量化方法。这项技术适用于灰度级扫描电子显微镜(SEM)图像,使用斐济的Trainable Segmentation插件对图像进行分割和转换为黑白(B / W)图像。使用新颖的Matlab脚本处理生成的图像,该脚本由三种不同的算法组成:分离算法,滤波和量化算法以及方向算法。最初,通过5个形态学过程增强了输入图像。使用源自每个节点的1个像素宽的等分线将生成的晶格“切割”为单个裂缝。使用连接组件方法标记裂缝,然后脚本计算几何参数,例如宽度,长度,面积,长宽比和方向。使用用户定义的纵横比值进行过滤,然后对剩余的裂缝进行统计分析。在本文的最后一部分,我们讨论了此脚本的效率,并介绍了一个分析两个具有不同维度和分辨率的数据集的示例。这些分析使用笔记本电脑和高端专业台式机解决方案进行,以模拟不同的工作环境。

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