General Defocusing Particle Tracking: fundamentals and uncertainty assessment
Rune Barnkob, Massimiliano Rossi

TL;DR
This paper provides a comprehensive analysis of General Defocusing Particle Tracking (GDPT), establishing fundamental principles, a standardized performance assessment framework, and guidelines for uncertainty evaluation in experimental measurements.
Contribution
It introduces a standardized synthetic image-based framework for assessing GDPT performance and provides guidelines for uncertainty quantification in experimental data.
Findings
Identified fundamental elements of GDPT measurements.
Developed a synthetic image framework for performance assessment.
Provided guidelines for uncertainty estimation in experiments.
Abstract
General Defocusing Particle Tracking (GDPT) is a single-camera, three-dimensional particle tracking method that determines the particle depth positions from the defocusing patterns of the corresponding particle images. GDPT relies on a reference set of experimental particle images which is used to predict the depth position of measured particle images of similar shape. While several implementations of the method are possible, its accuracy is ultimately limited by some intrinsic properties of the acquired data, such as the signal-to-noise ratio, the particle concentration, as well as the characteristics of the defocusing patterns. GDPT has been applied in different fields by different research groups, however, a deeper description and analysis of the method fundamentals has hitherto not been available. In this work, we first identity the fundamental elements that characterize a GDPT…
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