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Machine Learning and Computer Vision for the Classification of Carbon Nanotube and Nanofiber Structures from Transmission Electron Microscopy Data

Published online by Cambridge University Press:  05 August 2019

Thomas Matson ,
Max Farfel ,
Nathan Levin ,
Elizabeth Holm  and
Chen Wang
Thomas Matson*
Affiliation:
Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Max Farfel
Affiliation:
Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Nathan Levin
Affiliation:
Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Elizabeth Holm
Affiliation:
Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Chen Wang
Affiliation:
Division of Applied Research and Technology, National Institute for Occupational Safety and Health, Cincinnati, Ohio, USA.
*
*Corresponding Author: [email protected]

Abstract

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Type
Data Acquisition Schemes, Machine Learning Algorithms, and Open Source Software Development for Electron Microscopy
Information
Microscopy and Microanalysis , Volume 25 , Supplement S2 , August 2019 , pp. 198 - 199
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]Oberdörster, G, Castranova, V and Asgharian, B, Journal of Toxicology and Environmental Health, Part B 18 (2015), p. 121.Google Scholar
[2]NIOSH in “Current Intelligence Bulletin 65: Occupational Exposure to Carbon Nanotubes and Nanofibers”, Cincinnati, OH, US DHHS (NIOSH), Pub. No. 2013–145.Google Scholar
[3]Birch, M, Wang, C and Fernback, J, NIOSH Manual of Analytical Methods 5 (2017).Google Scholar
[4]Hariharan, B et al. , eprint arXiv:14115752, (2014), p. arXiv:1411.5752.Google Scholar
[5]J'egou, H et al. , Proceedings of CVPR (2010) p. 3304.Google Scholar
[6]The authors acknowledge funding from the Carnegie Mellon University Department of Materials Science Senior Capstone Program, and the help of Professor Robert A. Heard.Google Scholar