Abstract
A neural network is proposed for the recognition of partially overlapped particle images in the analysis of Particle Tracking Velocimetry (PTV) frames. The Kohonen neural network is an approximation to an optimum classifier. In this work it allows single particle images to be distinguished from overlapped particle images by shape analysis: it classifies 99.1% of the spots correctly (in test images). If a spot has an almost circular shape, the barycenter co-ordinates are extracted. If the spot shape is far from being circular, it is believed to be a particle overlap, and a procedure to find more centroids is activated.
The particle recognizer based on the Kohonen neural network is tested on both multi-exposed and single-exposure images at high particle density, and compared to a particle recognizer that did not consider the partial overlap. The management of overlapped particles causes the neural network to produce a big improvement in the number of barycenters that can be extracted from these images. The practical consequence is that the seeding density in PTV can be increased, so as to improve the spatial resolution of the technique in the velocity field calculation.
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Marcello Sallusti and Paolo Monti produced the synthetic PTV images that were used in this project. Gianni Leuzzi helped clearing the statistic properties of the neural network through infinite discussions. We would like to thank them all, since we are aware that they all gave a decisive contribute to the project.
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Carosone, F., Cenedese, A. & Querzoli, G. Recognition of partially overlapped particle images using the Kohonen neural network. Experiments in Fluids 19, 225–232 (1995). https://doi.org/10.1007/BF00196470
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DOI: https://doi.org/10.1007/BF00196470