A high-speed tracking algorithm for dense granular media
Author
Abstract
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 10× when compared to a sequential
CPU implementation in C and up to 40× 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.
Indexation
Artículo de publicación SCOPUS
Identifier
URI: https://repositorio.uchile.cl/handle/2250/169274
DOI: 10.1016/j.cpc.2018.02.010
ISSN: 00104655
Quote Item
Computer Physics Communications, Volumen 227, 2018, Pages 8–16
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