Authors:
Wissam AlKendi
1
;
Paresh Mahapatra
2
;
Bassam Alkindy
3
;
Christophe Guyeux
2
and
Magali Barthès
2
Affiliations:
1
UTBM, CIAD, F-90010 Belfort, France
;
2
FEMTO-ST Institute, Univ. Franche-Comté, CNRS, 15 B Avenue des Montboucons, Besançon, France
;
3
Department of Computer Science, College of Science, Mustansiriyah University, 10052 Baghdad, Iraq
Keyword(s):
Particle Detection, Particle Overlap, non-Local Means, Laplacian of Gaussian.
Abstract:
The study of fluid flows concerns many fields (e.g., biology, aeronautics, chemistry). To overcome the problems of flow disturbances caused by intrusive physical sensors, different methods of flow quantification, based on optical visualization, are particularly interesting. Among them, PTV (Particle Tracking Velocimetry) which allows the individualized tracking of tracers/particles, is of growing interest. Different numerical treatments will enable us to identify and track the particles. However, detection algorithms (e.g., Sobel, Canny, Robert, Gaussian, morphology) can be sensitive to noise and the phenomenon of overlapping particles in flow. In this work, we have focused on the detection part with the objective of improving it as much as possible. To quantify the performance of the different methods tested, synthetic images, with well-defined parameters have been generated. We compared the performances of the Laplacian of Gaussian (LoG) and the Difference of Gaussian (DoG) methods
, with the traditional method of threshold binarization. In addition, we tested other techniques based on non-local means (NLM) and overlapping detector to improve the detection of particles in case of noisy images or overlapping particles. The results show that the LoG gives very good results in most cases, with additional improvement when using the NLM and the overlap detector.
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