Fitting Laguerre tessellation approximations to tomographic image data. Issue 2 (12th January 2016)

• Record Type: Journal Article
• Authors: Spettl, A.; Brereton, T.; Duan, Q.; Werz, T.; Krill, C. E.; Kroese, D. P.; Schmidt, V.
• Is Part Of: Philosophical magazine. Volume 96:Issue 2(2016)
• Page Start: 166
• Page End: 189
• Publication Date: 2016-01-12
• Subjects: Image processing -- microstructural characterization -- grain boundary structure -- polycrystalline -- power diagram -- inverse problem -- cross-entropy method; Condensed matter -- Periodicals; Physics -- Periodicals; Matière condensée -- Périodiques; Physique -- Périodiques; 530.41
• DOI: 10.1080/14786435.2015.1125540
• Languages: English
• ISSNs: 1478-6435

Abstract

Abstract : 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.