Hostname: page-component-76fb5796d-r6qrq Total loading time: 0 Render date: 2024-04-29T05:18:14.294Z Has data issue: false hasContentIssue false

Robust Deep-learning Based Autofocus Score Prediction for Scanning Electron Microscope

Published online by Cambridge University Press:  30 July 2020

Hyun Jong Yang
Affiliation:
EgoVid Inc., Ulsan, Ulsan-gwangyoksi, Republic of Korea Coxem, Daejeon, Taejon-jikhalsi, Republic of Korea
Moohyun Oh
Affiliation:
EgoVid Inc., Ulsan, Ulsan-gwangyoksi, Republic of Korea
Jonggyu Jang
Affiliation:
Coxem, Daejeon, Taejon-jikhalsi, Republic of Korea
Hyeonsu Lyu
Affiliation:
Coxem, Daejeon, Taejon-jikhalsi, Republic of Korea
Junhee Lee
Affiliation:
Ulsan National Institute of Science and Technology, Ulsan, Ulsan-gwangyoksi, Republic of Korea

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

De Nobili, Cristiano, (2017) Deep Learning for Nanoscience Scanning Electron Microscope Image Recognition in Master in High Performance Computing (MHPC 2017.)Google Scholar
Kim, H., (2019) Deep-Learning Based Autofocus Score Prediction of Scanning Electron Microscope in Microscopy & MicroAnalysis, 2019. (M&M 2019).Google Scholar
Groen, Frans C.A., (1985) A Comparison of Different Focus Functions for Use in Autofocus Algorithms in in Journal of Microscopy, 6 p. 81-91Google Scholar
Santos, A., (1997) Evaluation of Autofocus Functions in Molecular Cytogenetic Analysis in Journal of Microscopy, 188 p. 264-272.10.1046/j.1365-2818.1997.2630819.xCrossRefGoogle ScholarPubMed
Yao, Y., (2006) Granular Computing for Data Mining in Society of Photographic Instrumentation Engineers (SPIE 2006) p. 1-12.Google Scholar
He, K., (2016) Deep Residual Learning for Image Recognition in Conference on Computer Vision and Pattern Recognition (IEEE CVPR 2016).10.1109/CVPR.2016.90CrossRefGoogle Scholar