Randomized Computer Vision Approaches for Pattern Recognition in Timepix and Timepix3 Detectors
- NázevTitle
- Randomized Computer Vision Approaches for Pattern Recognition in Timepix and Timepix3 DetectorsRandomized Computer Vision Approaches for Pattern Recognition in Timepix and Timepix3 Detectors
- Druh výsledkuResult type
- Příspěvek ve sborníkuProceedings paper
- AutořiAuthors
- P. Mánek, B. Bergmann, P. Burian, L. Meduna, S. Pospíšil, M. Suk
- Klíčová slovaKeywords
- Timepix3, Timepix, RANSAC, Hough Transform, Simulated Annealing, atlas, atlas-tpx, MoEDAL, Randomized Methods, particle identification, Trajectory Fitting, Segmentation
- DOIDOI
- 10.48550/arXiv.1911.02367
- Časopis / citaceJournal / citation
- In: Proceedings of Connecting the Dots and Workshop on Intelligent Trackers 2019. ArXiv, 2019.
- RokYear
- 2019
- JazykLanguage
- eng
- ZáznamyRecords
- ProjektProject
- CERN-CZ - Výzkumná infrastruktura pro experimenty v CERN - LM2015058 (2016–2019)CERN-CZ - Výzkumná infrastruktura pro experimenty v CERN - LM2015058 (2016–2019); Urychlovač Van de Graaff - laditelný zdroj monoenergetických neutronů a lehkých iontůVan de Graaff Accelerator - a Tunable Source of Monoenergetic Neutrons and Light Ions
- CitovánoCited by
- 6 (OpenAlex)
- Plný text (open access)Full text (open access)
- https://arxiv.org/pdf/1911.02367
- Citace ke staženíDownload citation
- TXT · BibTeX
AbstraktAbstract
Timepix and Timepix3 are hybrid pixel detectors (256×256 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.
Timepix and Timepix3 are hybrid pixel detectors (256×256 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.