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“Smart Microscopy”: Feature Based Adaptive Sampling for Focused Ion Beam Scanning Electron Microscopy

Published online by Cambridge University Press:  25 July 2016

Tim Dahmen
Affiliation:
German Research Center for Artificial Intelligence, Saarbrucken, Germany
Niels de Jonge
Affiliation:
INM - Leibniz Institute for New Materials, Saarbrucken, Germany
Patrick Trampert
Affiliation:
German Research Center for Artificial Intelligence, Saarbrucken, Germany Saarland University, Saarbrucken, Germany
Michael Engstler
Affiliation:
Saarland University, Saarbrucken, Germany
Christoph Pauly
Affiliation:
Saarland University, Saarbrucken, Germany
Frank Mücklich
Affiliation:
Saarland University, Saarbrucken, Germany
Philipp Slusallek
Affiliation:
German Research Center for Artificial Intelligence, Saarbrucken, Germany Saarland University, Saarbrucken, Germany

Abstract

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Type
Abstract
Copyright
© Microscopy Society of America 2016 

References

References:

[1] Dahmen, Tim, et. al., Feature Adaptive Sampling for Scanning Electron Microscopy, under review Nature: Scientific Reports.Google Scholar
[2] Dahmen, T. & de Jonge, N. Verfahren und Vorrichtung zur Untersuchung von Proben durch ein Elektronen- oder Ionenstrahlmikroskop. German Patent No 10 2015 114(843), 9 (2015).Google Scholar
[3] Weickert, J. Anisotropic diffusion in image processing. Image Rochester NY 256, 170 (1998).Google Scholar
[4] The authors acknowledge funding from European Research Project NOTOX (FP7-267038), the DFG grant IMCL (AOBJ: 600875) and the “Landesforschungsforderungsprogramm des Saarlandes” (WT/2- LFFP 15/09). The authors thank the DFKI GmbH, and Saarland University for additional funding and for providing the necessary infrastructure, and E. Arzt for his support through INM.Google Scholar