基于形态学扭曲度平均变化的复杂多孔结构表征非均质性评价

IF 0.8 4区 计算机科学 Q4 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY Image Analysis & Stereology Pub Date : 2020-06-22 DOI:10.5566/ias.2370
Johan Chaniot, M. Moreaud, L. Sorbier, D. Jeulin, J. Becker, T. Fournel
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引用次数: 1

摘要

多孔介质的形态表征是最重要的兴趣,主要是由于它们的物理化学性质和它们的多孔微观结构几何之间的联系。非均质性可以看作是多孔微结构的一个几何特征。本文基于m -扭曲的形式主义,提出了两种新的拓扑描述符。利用几何扭曲度或形态扭曲度的概念,定义了第一算子h -扭曲度。它估计的平均变化形态扭曲作为尺度的函数,基于蒙特卡罗方法和评估多孔网络的异质性。第二个描述符是一个扩展,命名为h -迭代侵蚀扭曲,考虑到不同的渗透颗粒尺寸。将这两种拓扑算子应用于Cox多尺度布尔模型,验证了它们的行为并突出了它们的判别能力。
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HETEROGENEITY ASSESSMENT BASED ON AVERAGE VARIATIONS OF MORPHOLOGICAL TORTUOSITY FOR COMPLEX POROUS STRUCTURES CHARACTERIZATION
Morphological characterization of porous media is of paramount interest, mainly due to the connections between their physicochemical properties and their porous microstructure geometry. Heterogeneity can be seen as a geometric characteristic of porous microstructures. In this paper, two novel topological descriptors are proposed, based on the M-tortuosity formalism. Using the concept of geometric tortuosity or morphological tortuosity, a first operator is defined, the H-tortuosity . It estimates the average variations of the morphological tortuosity as a function of the scale, based on Monte Carlo method and assessing the heterogeneity of porous networks. The second descriptor is an extension, named the H-tortuosity-by-iterativeerosions , taking into account different percolating particle sizes. These two topological operators are applied on Cox multi-scale Boolean models, to validate their behaviors and to highlight their discriminative power.
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来源期刊
Image Analysis & Stereology
Image Analysis & Stereology MATERIALS SCIENCE, MULTIDISCIPLINARY-MATHEMATICS, APPLIED
CiteScore
2.00
自引率
0.00%
发文量
7
审稿时长
>12 weeks
期刊介绍: Image Analysis and Stereology is the official journal of the International Society for Stereology & Image Analysis. It promotes the exchange of scientific, technical, organizational and other information on the quantitative analysis of data having a geometrical structure, including stereology, differential geometry, image analysis, image processing, mathematical morphology, stochastic geometry, statistics, pattern recognition, and related topics. The fields of application are not restricted and range from biomedicine, materials sciences and physics to geology and geography.
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