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Real-Time Imaging
Article . 2001 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
zbMATH Open
Article . 2001
Data sources: zbMATH Open
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Article . 2001
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DBLP
Article . 2024
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A Vision-Based Particle Tracking Velocimetry

A vision-based particle tracking velocimetry
Authors: NESI, PAOLO; DE LUCIA, MAURIZIO; ROSSI, FRANCESCA; BALDASSARRE, ANTONIO;

A Vision-Based Particle Tracking Velocimetry

Abstract

Summary: Particle Image Velocimetry (PIV) is a non-intrusive optical technique to measure velocity of flows. It provides the simultaneous visualization of the streamline pattern in unsteady flows and the quantification of the velocity field over the image plane. To reveal the flow motion, the flow is seeded by small scattering particles. The instantaneous fluid velocities are evaluated by recording the images of tracers, suspended in the fluid and traversing a light sheet. A PIV system consists of seeding particles, illumination unit, image acquisition system, and a computer for image processing. For industrial applications a classical PIV system is not suitable for its cost, sizes and needs of specialised users and work areas. Moreover, classical PIV are unable to work in real-time for the huge amount of data and expensive algorithms adopted. In this paper, a study and the implementation of a new PIV system is described and compared against classical PIV solutions. The solution proposed is capable of working in real-time and is a Continuous PIV, CPIV, system: with respect to classical PIV, it is composed of a continuous laser light source and a CCD camera. A specifically new image-processing algorithm for velocity estimation and recognition of correct traces has been developed. It is based on the grey level distribution in the particle trace image, and indicates those particles moving with an out-of-plane velocity vector component and provides the measure with a limited error.

Country
Italy
Keywords

Computing methodologies and applications, Computing methodologies for image processing, Machine vision and scene understanding

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Average
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