ArticleSensors (Basel, Switzerland)2021
Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- On the Search for Potentially Anomalous Traces of Cosmic Ray Particles in Images Acquired by Cmos Detectors for a Continuous Stream of Emerging Observational Data.Sensors (Basel, Switzerland) · 2024Article
- MRNG: Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator.Entropy (Basel, Switzerland) · 2023Article
- The Practice of Detecting Potential Cosmic Rays Using CMOS Cameras: Hardware and Algorithms.Sensors (Basel, Switzerland) · 2023Article
- MTF Measurement by Slanted-Edge Method Based on Improved Zernike Moments.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
12 authors.
Funding
Abstract
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%.
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