Machine Learning Techniques for the Segmentation of Tomographic Image Data of Functional Materials

Machine Learning Techniques for the Segmentation of Tomographic Image Data of Functional Materials

In this paper, various kinds of applications are presented, in which tomographic image data depicting microstructures of materials are semantically segmented by combining machine learning methods and conventional image processing steps. The main focus of this paper is the grain-wise segmentation of...

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Bibliographic Details
Main Authors: Orkun Furat, Mingyan Wang, Matthias Neumann, Lukas Petrich, Matthias Weber, Carl E. Krill, Volker Schmidt
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-06-01
Series:Frontiers in Materials
Subjects:
machine learning
segmentation
X-ray microtomography
polycrystalline microstructure
Ostwald ripening
statistical image analysis
Online Access:https://www.frontiersin.org/article/10.3389/fmats.2019.00145/full

Internet

https://www.frontiersin.org/article/10.3389/fmats.2019.00145/full

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