Hachaj, Tomasz ; Piekarczyk, Marcin ; Wąs, Jarosław
Współtwórca:Campagner, Andrea - ed. ; Lenz, Oliver Urs - ed. ; Xia, Shuyin - ed.
Tytuł: Podtytuł: Tytuł publikacji grupowej: Temat i słowa kluczowe:cosmic-ray particle ; rough sets ; rough k-means ; anomalies detection ; principal components analysis ; complementary metal-oxide-semiconductor sensors
Abstract:We present a method capable of detecting potentially anomalous cosmic particle tracks acquired with complementary metal-oxide-semiconductor (CMOS) sensors. We apply a principal components analysis-based feature extraction method and rough k-means clustering for outlier detection. We evaluated our approach on more than ten to the fourth power images acquired by the Cosmic Ray Extremely Distributed Observatory (CREDO). The method presented in this work proved to be an effective solution. ; The analysis of the behavior of the rough k-means clustering-based algorithm presented here and the method of selecting its parameters showed that the algorithm performs as expected and demonstrates efficiency, stability, and repeatability of results for the test data set. The results included in this work are very relevant to the international CREDO project and the broader problem of anomaly analysis in image data sets. We plan to deploy the presented methodology in the image processing pipeline of the large data set we are working on in the CREDO project. The results can be reproduced using our source code, which is published in an open repository.
Wydawca:Zielona Góra: Uniwersytet Zielonogórski
Data wydania: Typ zasobu: DOI: Strony: Źródło:AMCS, volume 35, number 1 (2025) ; kliknij tutaj, żeby przejść
Jezyk: Licencja CC BY 4.0: Prawa do dysponowania publikacją: