Semantic Segmentation of Multi-Channel Polycrystalline Structure Micrographs Using Convolutional Neural Networks

Selmaier A, Lutz B, Kißkalt D, Boernicke S, Fuerst J, Franke J (2021)

Publication Type: Conference contribution

Publication year: 2021

Publisher: IEEE

City/Town: NEW YORK

Pages Range: 847-852

Conference Proceedings Title: 20TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2021)

Event location: , ELECTR NETWORK

DOI: 10.1109/ICMLA52953.2021.00140

Abstract

Due to ever-increasing data availability, computational power, and algorithmic advances, machine learning enables various novel industrial applications. A field with particularly strong potential for machine learning is computer vision. As part of that area, semantic segmentation refers to a process that links each pixel of an image to a corresponding class. In this task, deep learning has outperformed traditional image processing techniques as well as other classical machine learning techniques and therefore has become the new standard approach. Convolutional neural networks (CNN) are a specific form of deep neural networks, which exploit spatial information for the classification of pixels. This paper presents an approach in which CNNs are optimized and applied to a semantic segmentation problem by using multichannel microscopic images of tungsten flat emitters to determine their surface conditions. These are of strong interest since they may reveal information about potential defects and the life expectancy of the emitter, which is an indispensable component of X-ray tubes as they are used in medical applications.

Authors with CRIS profile

Andreas Selmaier Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Benjamin Lutz Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Dominik Kißkalt Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Jörg Franke Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik

Involved external institutions

Siemens AG, Healthcare Sector DE Germany (DE)

How to cite

APA:

Selmaier, A., Lutz, B., Kißkalt, D., Boernicke, S., Fuerst, J., & Franke, J. (2021). Semantic Segmentation of Multi-Channel Polycrystalline Structure Micrographs Using Convolutional Neural Networks. In 20TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2021) (pp. 847-852). , ELECTR NETWORK: NEW YORK: IEEE.

MLA:

Selmaier, Andreas, et al. "Semantic Segmentation of Multi-Channel Polycrystalline Structure Micrographs Using Convolutional Neural Networks." Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA), , ELECTR NETWORK NEW YORK: IEEE, 2021. 847-852.

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