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  Segmentation of Static and Dynamic Atomic-Resolution Microscopy Data Sets with Unsupervised Machine Learning Using Local Symmetry Descriptors

Wang, N., Freysoldt, C., Zhang, S., Liebscher, C., & Neugebauer, J. (2021). Segmentation of Static and Dynamic Atomic-Resolution Microscopy Data Sets with Unsupervised Machine Learning Using Local Symmetry Descriptors. Microscopy and Microanalysis, 27(6), 1454-1464. doi:10.1017/S1431927621012770.

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segmentation-of-static-and-dynamic-atomic-resolution-microscopy-data-sets-with-unsupervised-machine-learning-using-local-symmetry-descriptors.pdf (Publisher version), 930KB
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segmentation-of-static-and-dynamic-atomic-resolution-microscopy-data-sets-with-unsupervised-machine-learning-using-local-symmetry-descriptors.pdf
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2021
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 Creators:
Wang, Ning1, Author              
Freysoldt, Christoph1, Author              
Zhang, Siyuan2, Author              
Liebscher, Christian3, Author              
Neugebauer, Jörg4, Author              
Affiliations:
1Defect Chemistry and Spectroscopy, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863342              
2Nanoanalytics and Interfaces, Independent Max Planck Research Groups, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_2054294              
3Advanced Transmission Electron Microscopy, Structure and Nano-/ Micromechanics of Materials, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863399              
4Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863337              

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 Abstract: We present an unsupervised machine learning approach for segmentation of static and dynamic atomic-resolution microscopy data sets in the form of images and video sequences. In our approach, we first extract local features via symmetry operations. Subsequent dimension reduction and clustering analysis are performed in feature space to assign pattern labels to each pixel. Furthermore, we propose the stride and upsampling scheme as well as separability analysis to speed up the segmentation process of image sequences. We apply our approach to static atomic-resolution scanning transmission electron microscopy images and video sequences. Our code is released as a python module that can be used as a standalone program or as a plugin to other microscopy packages. Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America.

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Language(s): eng - English
 Dates: 2021-09-212021-12
 Publication Status: Published in print
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1017/S1431927621012770
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Title: Microscopy and Microanalysis
  Abbreviation : Microsc. Microanal.
Source Genre: Journal
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Publ. Info: New York, NY : Cambridge University Press
Pages: 11 Volume / Issue: 27 (6) Sequence Number: - Start / End Page: 1454 - 1464 Identifier: ISSN: 1431-9276
CoNE: https://pure.mpg.de/cone/journals/resource/991042731793414