Fitting Laguerre tessellation approximations to tomographic image data

Spettl, A., Brereton, T., Duan, Q., Werz, T., Krill, C. E., Kroese, D. P. and Schmidt, V. (2016) Fitting Laguerre tessellation approximations to tomographic image data. Philosophical Magazine, 96 2: 166-189. doi:10.1080/14786435.2015.1125540


Author Spettl, A.
Brereton, T.
Duan, Q.
Werz, T.
Krill, C. E.
Kroese, D. P.
Schmidt, V.
Title Fitting Laguerre tessellation approximations to tomographic image data
Journal name Philosophical Magazine   Check publisher's open access policy
ISSN 1478-6443
1478-6435
Publication date 2016-01-12
Sub-type Article (original research)
DOI 10.1080/14786435.2015.1125540
Open Access Status Not Open Access
Volume 96
Issue 2
Start page 166
End page 189
Total pages 24
Place of publication Abingdon, Oxfordshire, United Kingdom
Publisher Taylor & Francis
Collection year 2017
Language eng
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.
Keyword Cross-entropy method
Grain boundary structure
Image processing
Inverse problem
Microstructural characterization
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
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