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Authors: Stephen Kockentiedt 1 ; Klaus Tönnies 2 ; Erhardt Gierke 3 ; Nico Dziurowitz 3 ; Carmen Thim 3 and Sabine Plitzko 3

Affiliations: 1 Otto von Guericke University Magdeburg and German Federal Institute for Occupational Safety and Health, Germany ; 2 Otto von Guericke University Magdeburg, Germany ; 3 German Federal Institute for Occupational Safety and Health, Germany

ISBN: 978-989-758-090-1

Keyword(s): Computer Vision, Machine Learning, Nanoparticles, Particle Classification, Scanning Electron Microscopy.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis ; Segmentation and Grouping

Abstract: The amount of engineered nanoparticles produced each year has grown for some time and will grow in the coming years. However, if such particles are inhaled, they can be toxic. Therefore, to ensure the safety of workers, the nanoparticle concentrations at workplaces have to be measured. This is usually done by gathering the particles in the ambient air and then taking images using scanning electron microscopy. The particles in the images are then manually identified and counted. However, this task takes much time. Therefore, we have developed a system to automatically find and classify particles in these images (Kockentiedt et al., 2012). In this paper, we present an improved version of the system with two new classification feature types. The first are Haralick features. The second is a newly developed feature which estimates the counts of electrons detected by the scanning electron microscopy for each particle. In addition, we have added an algorithm to automatically choose the class ifier type and parameters. This way, no expert is needed when the user wants to train the system to recognize a previously unknown particle type. The improved system yields much better results for two types of engineered particles and shows comparable results for a third type. (More)

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Paper citation in several formats:
Kockentiedt, S.; Tönnies, K.; Gierke, E.; Dziurowitz, N.; Thim, C. and Plitzko, S. (2015). Improved Automatic Recognition of Engineered Nanoparticles in Scanning Electron Microscopy Images.In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 337-344. DOI: 10.5220/0005299003370344

@conference{visapp15,
author={Stephen Kockentiedt. and Klaus Tönnies. and Erhardt Gierke. and Nico Dziurowitz. and Carmen Thim. and Sabine Plitzko.},
title={Improved Automatic Recognition of Engineered Nanoparticles in Scanning Electron Microscopy Images},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={337-344},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005299003370344},
isbn={978-989-758-090-1},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - Improved Automatic Recognition of Engineered Nanoparticles in Scanning Electron Microscopy Images
SN - 978-989-758-090-1
AU - Kockentiedt, S.
AU - Tönnies, K.
AU - Gierke, E.
AU - Dziurowitz, N.
AU - Thim, C.
AU - Plitzko, S.
PY - 2015
SP - 337
EP - 344
DO - 10.5220/0005299003370344

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