Open AccessOn the uncertainty of defocus methods for 3D particle tracking velocimetry
Author(s) -
Christian Cierpka,
Sebastian Sachs,
Patrick Mäder,
Minqian Chen,
Rune Barnkob,
Massimiliano Rossi
Publication year - 2021
Publication title -
international symposium on particle image velocimetry.
Language(s) - English
Resource type - Journals
ISSN - 2769-7576
DOI - 10.18409/ispiv.v1i1.80
Subject(s) - particle tracking velocimetry , lens (geology) , computer science , computer vision , tracking (education) , artificial intelligence , position (finance) , particle (ecology) , ambiguity , volume (thermodynamics) , particle image velocimetry , optics , physics , geology , pedagogy , oceanography , finance , quantum mechanics , turbulence , economics , thermodynamics , programming language , psychology
Defocus methods have become more and more popular for the estimation of the 3D position of particles in flows (Cierpka and Kahler, 2011; Rossi and K ¨ ahler, 2014). Typically the depth positions of particles are ¨ determined by the defocused particle images using image processing algorithms. As these methods allow the determination of all components of the velocity vector in a volume using only a single optical access and a single camera, they are often used in, but not limited to microfluidics. Since almost no additional equipment is necessary they are low-cost methods that are meanwhile widely applied in different fields. To overcome the ambiguity of perfect optical systems, often a cylindrical lens is introduced in the optical system which enhances the differences of the obtained particle images for different depth positions. However, various methods are emerging and it is difficult for non-experienced users to judge what method might be best suited for a given experimental setup. Therefore, the aim of the presentation is a thorough evaluation of the performance of general advanced methods, including also recently presented neural networks (Franchini and Krevor, 2020; Konig et al., 2020) based on typical images.