Fitting Laguerre tessellation approximations to tomographic image data

Fitting Laguerre tessellation approximations to tomographic image data
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将拉盖尔曲面细分近似拟合到断层扫描图像数据

DOI:
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发表时间:
2015
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影响因子:
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通讯作者:
V. Schmidt
V. Schmidt
中科院分区:
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文献类型:
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作者:
A. Spettl;T. Brereton;Q. Duan;Thomas Werz;C. Krill;Dirk P. Kroese;V. Schmidt

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多晶材料的分析极大地得益于对其晶粒结构的精确定量描述。拉盖尔镶嵌非常接近这种晶粒结构。然而,这是一个相当具有挑战性的问题,以适应拉盖尔曲面细分的层析数据,作为一个高维优化问题,许多局部极小值必须解决。在本文中,我们制定了一个版本的优化问题,可以快速解决使用交叉熵方法,一个强大的随机优化技术,可以避免陷入局部极小值。我们证明了我们的方法的有效性,将其应用到人工生成和实验产生的断层数据。
The analysis of polycrystalline materials benefits greatly from accurate quantitative descriptions of their grain structures. Laguerre tessellations approximate such grain structures very well. However, it is a quite challenging problem to fit a Laguerre tessellation to tomographic data, as a high-dimensional optimization problem with many local minima must be solved. In this paper, we formulate a version of this optimization problem that can be solved quickly using the cross-entropy method, a robust stochastic optimization technique that can avoid becoming trapped in local minima. We demonstrate the effectiveness of our approach by applying it to both artificially generated and experimentally produced tomographic data.
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