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.
This work has been partially supported by the project of the Italian Ministry of Education, Universities and Research (MIUR) “Dipartimenti di Eccellenza 2018-2022”, and has been partially supported by the POR FESR 2014-2020 Work Program (Action 1.1.4, project No.10066183).
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Lanza, N., Romeo, A., Cristani, M., Setti, F. (2019). Grain Segmentation in Atomic Force Microscopy for Thin-Film Deposition Quality Control. In: Cristani, M., Prati, A., Lanz, O., Messelodi, S., Sebe, N. (eds) New Trends in Image Analysis and Processing – ICIAP 2019. ICIAP 2019. Lecture Notes in Computer Science(), vol 11808. Springer, Cham. https://doi.org/10.1007/978-3-030-30754-7_38
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