Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors

Reliable tools for artefact rejection and signal classification are a must for cosmic ray detection experiments based on CMOS technology. In this paper, we analyse the fitness of several feature-based statistical classifiers for the classification of particle candidate hits in four categories: spots...

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Autores principales:	Olaf Bar, Łukasz Bibrzycki, Michał Niedźwiecki, Marcin Piekarczyk, Krzysztof Rzecki, Tomasz Sośnicki, Sławomir Stuglik, Michał Frontczak, Piotr Homola, David E. Alvarez-Castillo, Thomas Andersen, Arman Tursunov
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	CMOS sensors feature-based classification Zernike moments machine learning computer vision Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/e0d447a779a44da8978b952d679c1456
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id	oai:doaj.org-article:e0d447a779a44da8978b952d679c1456
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spelling	oai:doaj.org-article:e0d447a779a44da8978b952d679c14562021-11-25T18:58:44ZZernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors10.3390/s212277181424-8220https://doaj.org/article/e0d447a779a44da8978b952d679c14562021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7718https://doaj.org/toc/1424-8220Reliable tools for artefact rejection and signal classification are a must for cosmic ray detection experiments based on CMOS technology. In this paper, we analyse the fitness of several feature-based statistical classifiers for the classification of particle candidate hits in four categories: spots, tracks, worms and artefacts. We use Zernike moments of the image function as feature carriers and propose a preprocessing and denoising scheme to make the feature extraction more efficient. As opposed to convolution neural network classifiers, the feature-based classifiers allow for establishing a connection between features and geometrical properties of candidate hits. Apart from basic classifiers we also consider their ensemble extensions and find these extensions generally better performing than basic versions, with an average recognition accuracy of 88%.Olaf BarŁukasz BibrzyckiMichał NiedźwieckiMarcin PiekarczykKrzysztof RzeckiTomasz SośnickiSławomir StuglikMichał FrontczakPiotr HomolaDavid E. Alvarez-CastilloThomas AndersenArman TursunovMDPI AGarticleCMOS sensorsfeature-based classificationZernike momentsmachine learningcomputer visionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7718, p 7718 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	CMOS sensors feature-based classification Zernike moments machine learning computer vision Chemical technology TP1-1185
spellingShingle	CMOS sensors feature-based classification Zernike moments machine learning computer vision Chemical technology TP1-1185 Olaf Bar Łukasz Bibrzycki Michał Niedźwiecki Marcin Piekarczyk Krzysztof Rzecki Tomasz Sośnicki Sławomir Stuglik Michał Frontczak Piotr Homola David E. Alvarez-Castillo Thomas Andersen Arman Tursunov Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors
description	Reliable tools for artefact rejection and signal classification are a must for cosmic ray detection experiments based on CMOS technology. In this paper, we analyse the fitness of several feature-based statistical classifiers for the classification of particle candidate hits in four categories: spots, tracks, worms and artefacts. We use Zernike moments of the image function as feature carriers and propose a preprocessing and denoising scheme to make the feature extraction more efficient. As opposed to convolution neural network classifiers, the feature-based classifiers allow for establishing a connection between features and geometrical properties of candidate hits. Apart from basic classifiers we also consider their ensemble extensions and find these extensions generally better performing than basic versions, with an average recognition accuracy of 88%.
format	article
author	Olaf Bar Łukasz Bibrzycki Michał Niedźwiecki Marcin Piekarczyk Krzysztof Rzecki Tomasz Sośnicki Sławomir Stuglik Michał Frontczak Piotr Homola David E. Alvarez-Castillo Thomas Andersen Arman Tursunov
author_facet	Olaf Bar Łukasz Bibrzycki Michał Niedźwiecki Marcin Piekarczyk Krzysztof Rzecki Tomasz Sośnicki Sławomir Stuglik Michał Frontczak Piotr Homola David E. Alvarez-Castillo Thomas Andersen Arman Tursunov
author_sort	Olaf Bar
title	Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors
title_short	Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors
title_full	Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors
title_fullStr	Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors
title_full_unstemmed	Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors
title_sort	zernike moment based classification of cosmic ray candidate hits from cmos sensors
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e0d447a779a44da8978b952d679c1456
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_version_	1718410496620625920

Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors

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