Abstract
Particle recognition and particle matching are the core of particle tracking velocimetry (PTV). Particle recognition and matching directly determine the accuracy of flow field analysis, and particle recognition is a prerequisite for matching. In the engineering application of PTV technology, there are various reasons for the image noise generated by shooting, which seriously interferes with particle recognition. Accurately separating particles from noise is the most important and difficult process in particle recognition while ensuring that particle information is not damaged. According to the particle image captured by PTV experiment, the author has proposed a new processing method for denoising and recognition of noise-containing original image (ImageDenoising-ParticleRecognition). This method comprehensively uses SUSAN detection, expansion calculation, threshold segmentation, four-connected mark, non-particle culling, particle hole filling and other technologies to protect the image information of particles from loss to the maximum extent and ensure that the noise-containing particle images can complete particle recognition in one step. At the same time, the method proposed in this paper more accurately determines the edge of the particle image, and provides a more reliable particle image for the calculation of the flow field motion in the later stage. For the method proposed in this paper, based on the Visual C++ platform autonomous programming, the particle images generated by computer simulation and the SiO2 particle image with 500 nm diameter shot by the actual PTV experiment have been verified and analyzed respectively, and the recognition results with good accuracy have been obtained.
Graphic abstract
The preprocessing of particle image is the prerequisite of PTV processing, and its level directly has a crucial impact on the final analysis accuracy of the flow field. According to the particle image captured by PTV experiment, the author has proposed a new preprocessing method to provide a more reliable particle image for the calculation of the flow field motion in the later stage.
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Liu, Z., Li, J., Zhao, F. et al. Research on noise processing and particle recognition algorithm of PTV image. Granular Matter 22, 36 (2020). https://doi.org/10.1007/s10035-020-1005-4
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DOI: https://doi.org/10.1007/s10035-020-1005-4