TITLE:
Improved 3-D Particle Tracking Velocimetry with Colored Particles
AUTHORS:
Christian Bendicks, Dominique Tarlet, Christoph Roloff, Robert Bordás, Bernd Wunderlich, Bernd Michaelis, Dominique Thévenin
KEYWORDS:
Particle Tracking Velocimetry, Color Recognition, Artificial Neural Network, Photogrammetry
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.2 No.2,
May
26,
2011
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.