Randomized Computer Vision Approaches for Pattern Recognition in Timepix and Timepix3 Detectors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21670%2F19%3A00331872" target="_blank" >RIV/68407700:21670/19:00331872 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.48550/arXiv.1911.02367" target="_blank" >https://doi.org/10.48550/arXiv.1911.02367</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.48550/arXiv.1911.02367" target="_blank" >10.48550/arXiv.1911.02367</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Randomized Computer Vision Approaches for Pattern Recognition in Timepix and Timepix3 Detectors

Popis výsledku v původním jazyce

Timepix and Timepix3 are hybrid pixel detectors (256x256 pixels), capable of tracking ionizing particles as isolated clusters of pixels. To efficiently analyze such clusters at potentially high rates, we introduce multiple randomized pattern recognition algorithms inspired by computer vision. Offering desirable probabilistic bounds on accuracy and complexity, the presented methods are well-suited for use in real-time applications, and some may even be modified to tackle trans-dimensional problems. In Timepix detectors, which do not support data-driven acquisition, they have been shown to correctly separate clusters of overlapping tracks. In Timepix3 detectors, simultaneous acquisition of Time-of-Arrival (ToA) and Time-over-Threshold (ToT) pixel data enables reconstruction of the depth, transitioning from 2D to 3D point clouds. The presented algorithms have been tested on simulated inputs, test beam data from the Heidelberg Ion therapy Center and the Super Proton Synchrotron and were applied to data acquired in the MoEDAL and ATLAS experiments at CERN.

Název v anglickém jazyce

Randomized Computer Vision Approaches for Pattern Recognition in Timepix and Timepix3 Detectors

Popis výsledku anglicky

Timepix and Timepix3 are hybrid pixel detectors (256x256 pixels), capable of tracking ionizing particles as isolated clusters of pixels. To efficiently analyze such clusters at potentially high rates, we introduce multiple randomized pattern recognition algorithms inspired by computer vision. Offering desirable probabilistic bounds on accuracy and complexity, the presented methods are well-suited for use in real-time applications, and some may even be modified to tackle trans-dimensional problems. In Timepix detectors, which do not support data-driven acquisition, they have been shown to correctly separate clusters of overlapping tracks. In Timepix3 detectors, simultaneous acquisition of Time-of-Arrival (ToA) and Time-over-Threshold (ToT) pixel data enables reconstruction of the depth, transitioning from 2D to 3D point clouds. The presented algorithms have been tested on simulated inputs, test beam data from the Heidelberg Ion therapy Center and the Super Proton Synchrotron and were applied to data acquired in the MoEDAL and ATLAS experiments at CERN.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_013%2F0001785" target="_blank" >EF16_013/0001785: Urychlovač Van de Graaff - laditelný zdroj monoenergetických neutronů a lehkých iontů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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