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Journal of Signal and Information Processing
Article . 2011 . Peer-reviewed
License: CC BY
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Improved 3-D Particle Tracking Velocimetry with Colored Particles

Authors: Bendicks, C.; Tarlet, D.; Roloff, C.; Bordás, R.; Wunderlich, B.; Michaelis, B.; Thévenin, D.;

Improved 3-D Particle Tracking Velocimetry with Colored Particles

Abstract

The present work introduces an extension to three-dimensional Particle Tracking Velocimetry (3-D PTV) in order to investigate small-scale flow patterns. Instead of using monochrome particles the novelty over the prior state of the art is the use of differently dyed tracer particles and the identification of particle color classes directly on Bayer raw images. Especially in the case of a three camera setup it will be shown that the number of ambiguities is dramatically decreased when searching for homologous points in different images. This refers particularly to the determination of spatial parti- cle positions and possibly to the linking of positions into trajectories. The approach allows the handling of tracer parti- cles in high numbers and is therefore perfectly suited for gas flow investigations. Although the idea is simple, difficult- ties may arise particularly in determining the color class of individual particle when its projection on a Bayer sensor is too small. Hence, it is not recommended to extract features from RGB images for color class recognition due to infor- mation loss during the Bayer demosaicing process. This article demonstrates how to classify the color of small sized tracers directly on Bayer raw images.

  • BIP!
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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).
    16
    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.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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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!
16
Top 10%
Top 10%
Average
gold