BAR, O., Ł. BIBRZYCKI, M. NIEDŹWIECKI, M. PIEKARCZYK, K. RZECKI, T. SOŚNICKI, S. STUGLIK, M. FRONTCZAK, P. HOMOLA, D.E. ALVAREZ-CASTILLO, T. ANDERSEN a Arman TURSUNOV. Zernike moment based classification of cosmic ray candidate hits from cmos sensors. Sensors. Switzerland, 2021, roč. 21, č. 22, s. "7718-1"-"7718-18", 18 s. ISSN 1424-3210. Dostupné z: https://dx.doi.org/10.3390/s21227718. |
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@article{58484, author = {Bar, O. and Bibrzycki, Ł. and Niedźwiecki, M. and Piekarczyk, M. and Rzecki, K. and Sośnicki, T. and Stuglik, S. and Frontczak, M. and Homola, P. and AlvarezandCastillo, D.E. and Andersen, T. and Tursunov, Arman}, article_location = {Switzerland}, article_number = {22}, doi = {http://dx.doi.org/10.3390/s21227718}, keywords = {CMOS sensors; feature-based classification; Zernike moments; machine learning; computer vision}, language = {eng}, issn = {1424-3210}, journal = {Sensors}, title = {Zernike moment based classification of cosmic ray candidate hits from cmos sensors}, url = {https://www.mdpi.com/1424-8220/21/22/7718}, volume = {21}, year = {2021} }
TY - JOUR ID - 58484 AU - Bar, O. - Bibrzycki, Ł. - Niedźwiecki, M. - Piekarczyk, M. - Rzecki, K. - Sośnicki, T. - Stuglik, S. - Frontczak, M. - Homola, P. - Alvarez-Castillo, D.E. - Andersen, T. - Tursunov, Arman PY - 2021 TI - Zernike moment based classification of cosmic ray candidate hits from cmos sensors JF - Sensors VL - 21 IS - 22 SP - "7718-1"-"7718-18" EP - "7718-1"-"7718-18" SN - 14243210 KW - CMOS sensors KW - feature-based classification KW - Zernike moments KW - machine learning KW - computer vision UR - https://www.mdpi.com/1424-8220/21/22/7718 N2 - 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%. ER -
BAR, O., Ł. BIBRZYCKI, M. NIED$\backslash$'ZWIECKI, M. PIEKARCZYK, K. RZECKI, T. SO$\backslash$'SNICKI, S. STUGLIK, M. FRONTCZAK, P. HOMOLA, D.E. ALVAREZ-CASTILLO, T. ANDERSEN a Arman TURSUNOV. Zernike moment based classification of cosmic ray candidate hits from cmos sensors. \textit{Sensors}. Switzerland, 2021, roč.~21, č.~22, s.~''7718-1''-''7718-18'', 18 s. ISSN~1424-3210. Dostupné z: https://dx.doi.org/10.3390/s21227718.
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