Zernike moment based classification of cosmic ray candidate hits from cmos sensors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19630%2F21%3AA0000153" target="_blank" >RIV/47813059:19630/21:A0000153 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/1424-8220/21/22/7718" target="_blank" >https://www.mdpi.com/1424-8220/21/22/7718</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s21227718" target="_blank" >10.3390/s21227718</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Zernike moment based classification of cosmic ray candidate hits from cmos sensors

Popis výsledku v původním jazyce

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%.

Název v anglickém jazyce

Zernike moment based classification of cosmic ray candidate hits from cmos sensors

Popis výsledku anglicky

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%.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10308 - Astronomy (including astrophysics,space science)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Sensors

ISSN

1424-3210

e-ISSN

1424-8220

Svazek periodika

21

Číslo periodika v rámci svazku

22

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

18

Strana od-do

„7718-1“-„7718-18“

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85119323610