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2019 | OriginalPaper | Chapter

Grain Segmentation in Atomic Force Microscopy for Thin-Film Deposition Quality Control

Authors : Nicolò Lanza, Alessandro Romeo, Marco Cristani, Francesco Setti

Published in: New Trends in Image Analysis and Processing – ICIAP 2019

Publisher: Springer International Publishing

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Abstract

In this paper we propose an image segmentation method specifically designed to detect crystalline grains in microscopic images. We build on the watershed segmentation approach; we propose a preprocessing pipeline to generate a topographic map exploiting the physical nature of the incoming data (i.e. Atomic Force Microscopy) to emphasize grain boundaries and generate seeds for basins. Experimental results show the effectiveness of the proposed method against grain segmentation implementations available in commercial software on a new labelled dataset with an average improvement of over 20% in precision and recall over the standard implementation of watershed segmentation.

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http://vips.sci.univr.it/dataset/grainseg.

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http://vips.sci.univr.it/dataset/grainseg.

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Title: Grain Segmentation in Atomic Force Microscopy for Thin-Film Deposition Quality Control
Authors: Nicolò Lanza
Alessandro Romeo
Marco Cristani
Francesco Setti
Publisher: Springer International Publishing
Book: New Trends in Image Analysis and Processing – ICIAP 2019
Print ISBN: 978-3-030-30753-0

Electronic ISBN: 978-3-030-30754-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-30754-7_38

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