Journal
COMPUTER PHYSICS COMMUNICATIONS
Volume 227, Issue -, Pages 8-16Publisher
ELSEVIER
DOI: 10.1016/j.cpc.2018.02.010
Keywords
Particle tracking; Peak detection; GPU computing; Granular media
Funding
- Fondecyt [150393, 3160182, 11161033, 1151029]
- National Science Foundation [DMR-0303072, DMR-0807012]
- FONDEQUIP [EQM140119]
- Ring Initiative [ACT1402]
- Chilean Millennium Science Initiative [P09-015-F]
- CORFO [16CTTS-66390]
- DAAD [57220037, 57168868]
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Many fields of study, including medical imaging, granular physics, colloidal physics, and active matter, require the precise identification and tracking of particle-like objects in images. While many algorithms exist to track particles in diffuse conditions, these often perform poorly when particles are densely packed together-as in, for example, solid-like systems of granular materials. Incorrect particle identification can have significant effects on the calculation of physical quantities, which makes the development of more precise and faster tracking algorithms a worthwhile endeavor. In this work, we present a new tracking algorithm to identify particles in dense systems that is both highly accurate and fast. We demonstrate the efficacy of our approach by analyzing images of dense, solid-state granular media, where we achieve an identification error of 5% in the worst evaluated cases. Going further, we propose a parallelization strategy for our algorithm using a GPU, which results in a speedup of up to 10x when compared to a sequential CPU implementation in C and up to 40x when compared to the reference MATLAB library widely used for particle tracking. Our results extend the capabilities of state-of-the-art particle tracking methods by allowing fast, high-fidelity detection in dense media at high resolutions. (C) 2018 Elsevier B.V. All rights reserved.
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