Hostname: page-component-77c89778f8-gvh9x Total loading time: 0 Render date: 2024-07-18T23:16:27.676Z Has data issue: false hasContentIssue false

“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

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