Adaptive incremental stippling for sample distribution in spatially adaptive PIV image analysis

Adaptive incremental stippling for sample distribution in spatially adaptive PIV image analysis M Edwards http://orcid.org/0000-0002-7036-8789 R Theunissen http://orcid.org/0000-0001-5844-5158 Abstract

Adaptive sampling strategies in PIV have been shown to efficiently combine the need for limited user-dependence with increased performances in terms of spatial resolution and computational effort, thus rendering such approaches of great interest. The allocation of correlation windows across the spatial image domain is dependent on the interpretation of an underlying objective function, and the distribution of windows accordingly. It is important that such allocation is computationally efficient, robust to changing objective functions and conditions, and conducive to high quality sampling. In this paper, an alternative sample distribution method, based on adaptive incremental stippling, is presented and shown to combine the speed of PDF-based methods with the quality of ‘ideal’ spring-force methods. Case-dependent parameter tuning is no longer necessary, thus improving robustness. In addition, an algorithm to adaptively size initial correlation windows is proposed to further minimise user dependence.
05 10 2019 06 01 2019 065301 http://dx.doi.org/10.1088/crossmark-policy iopscience.iop.org Adaptive incremental stippling for sample distribution in spatially adaptive PIV image analysis Measurement Science and Technology paper © 2019 IOP Publishing Ltd 2018-11-20 2019-03-18 2019-05-10 Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266 https://doi.org/10.13039/501100000266 EP/M507994/1 http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining 10.1088/1361-6501/ab10b9 https://iopscience.iop.org/article/10.1088/1361-6501/ab10b9 http://iopscience.iop.org/article/10.1088/1361-6501/ab10b9/pdf https://iopscience.iop.org/article/10.1088/1361-6501/ab10b9/pdf https://iopscience.iop.org/article/10.1088/1361-6501/ab10b9 https://iopscience.iop.org/article/10.1088/1361-6501/ab10b9/pdf http://iopscience.iop.org/article/10.1088/1361-6501/ab10b9/pdf https://iopscience.iop.org/article/10.1088/1361-6501/ab10b9/pdf http://iopscience.iop.org/article/10.1088/1361-6501/ab10b9 http://iopscience.iop.org/article/10.1088/1361-6501/ab10b9/pdf https://iopscience.iop.org/article/10.1088/1361-6501/ab10b9/pdf https://iopscience.iop.org/article/10.1088/1361-6501/ab10b9 https://iopscience.iop.org/article/10.1088/1361-6501/ab10b9 https://iopscience.iop.org/article/10.1088/1361-6501/ab10b9/pdf Raffel 2007 Exp. 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