Abstract
Particle tracking velocimetry (PTV) gives quantitative estimates of fluid flow velocities from images. But particle tracking is a complicated problem, and it often produces results that need substantial post-processing. We propose a novel Gaussian process regression-based post-processing step for PTV: The method smooths ("denoises") and densely interpolates velocity estimates while rejecting track irregularities. The method works under a large range of particle densities and fluid velocities. In addition, the method calculates standard deviances (error bars) for the velocity estimates, opening the possibility of propagating the standard deviances through subsequent processing and analysis. The accuracy of the method is experimentally evaluated using Optical Coherence Tomography images of particles in laminar flow in a pipe phantom. Following this, the method is used to quantify cilia-driven fluid flow and vorticity patterns in a developing Xenopus embryo.
Highlights
Image-based particle tracking velocimetry (PTV) tracks freely floating particles in a fluid and uses the tracks to estimate the underlying fluid flow velocities [1,2,3]
In the fourth set of results, we explored the effect of varying the data density on the Gaussian process regression (GPR) estimate
Velocity estimates produced by the algorithm with the pipe phantom data and the Xenopus embryo data clearly demonstrate the power of the algorithm to interpolate over a variety of Particle Density Avg
Summary
Image-based particle tracking velocimetry (PTV) tracks freely floating particles in a fluid and uses the tracks to estimate the underlying fluid flow velocities [1,2,3]. The idea behind PTV is conceptually appealing, but in reality, particle tracking is a complicated affair. Many real-world phenomenon – such as the appearance and disappearance of particles – confuse the particle tracker, and corrupt its velocity estimates. PTV velocities need significant post-processing to be useful. This paper presents a Gaussian process regression (GPR) framework for such post-processing. Our method accepts noisy PTV output and produces improved fluid flow velocity estimates
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