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Mapping the Distortion Function via Multivariate Analysis of Atomically Resolved Images

Published online by Cambridge University Press:  30 July 2020

Kevin Roccapriore
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Matthew Chisholm
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Gerd Duscher
Affiliation:
The University of Tennessee Knoxville, Knoxville, Tennessee, United States
Sergei Kalinin
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Maxim Ziatdinov
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States

Abstract

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Type
Advances in Modeling, Simulation, and Artificial Intelligence in Microscopy and Microanalysis for Physical and Biological Systems
Copyright
Copyright © Microscopy Society of America 2020

References

Sang, X. & LeBeau, J. M. Revolving scanning transmission electron microscopy: Correcting sample drift distortion without prior knowledge. Ultramicroscopy 138, 2835 (2014).10.1016/j.ultramic.2013.12.004CrossRefGoogle ScholarPubMed
Ophus, C., Ciston, J. & Nelson, C. T. Correcting nonlinear drift distortion of scanning probe and scanning transmission electron microscopies from image pairs with orthogonal scan directions. Ultramicroscopy 162, 19 (2016).10.1016/j.ultramic.2015.12.002CrossRefGoogle ScholarPubMed
Rasmussen, C. E. Gaussian Processes in Machine Learning. in Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2 - 14, 2003, Tübingen, Germany, August 4 - 16, 2003, Revised Lectures (eds. Bousquet, O., von Luxburg, U. & Rätsch, G.) 63–71 (Springer, 2004). doi:10.1007/978-3-540-28650-9_4.Google Scholar