Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-12-05T07:20:35.806Z Has data issue: false hasContentIssue false

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

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
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