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基于快速多尺度聚类的推广实现 EBSD 数据的边界识别。

Boundary identification in EBSD data with a generalization of fast multiscale clustering.

机构信息

Department of Engineering, Harvey Mudd College, 301 Platt Blvd, Claremont, CA 91711, USA.

出版信息

Ultramicroscopy. 2013 Oct;133:16-25. doi: 10.1016/j.ultramic.2013.04.009. Epub 2013 May 2.

Abstract

Electron backscatter diffraction (EBSD) studies of cellular or subgrain microstructures present problems beyond those in the study of coarse-grained polycrystalline aggregates. In particular, identification of boundaries delineating some subgrain structures, such as microbands, cannot be accomplished simply with pixel-to-pixel misorientation thresholding because many of the boundaries are gradual transitions in crystallographic orientation. Fast multiscale clustering (FMC) is an established data segmentation technique that is combined here with quaternion representation of orientation to segment EBSD data with gradual transitions. This implementation of FMC addresses a common problem with segmentation algorithms, handling data sets with both high and low magnitude boundaries, by using a novel distance function that is a modification of Mahalanobis distance. It accommodates data representations, such as quaternions, whose features are not necessarily linearly correlated but have known distance functions. To maintain the linear run time of FMC with such data, the method requires a novel variance update rule. Although FMC was originally an algorithm for two-dimensional data segmentation, it can be generalized to analyze three-dimensional data sets. As examples, several segmentations of quaternion EBSD data sets are presented.

摘要

电子背散射衍射 (EBSD) 研究细胞或亚晶粒微观结构所面临的问题超出了粗晶粒多晶聚集态的研究范围。特别是,由于许多边界是晶体取向的渐变过渡,因此不能仅通过像素到像素的位向差阈值来确定微带等某些亚晶粒结构的边界。快速多尺度聚类 (FMC) 是一种成熟的数据分割技术,本文将其与四元数表示的取向相结合,用于分割具有渐变过渡的 EBSD 数据。该 FMC 实现解决了分割算法的一个常见问题,通过使用对马氏距离的修改来处理具有高和低幅度边界的数据集,从而适应了不一定具有线性相关性但具有已知距离函数的数据表示,例如四元数。为了保持这种数据的 FMC 的线性运行时间,该方法需要一个新的方差更新规则。尽管 FMC 最初是二维数据分割算法,但它可以推广到分析三维数据集。作为示例,给出了几个四元数 EBSD 数据集的分割。

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