Adaptive incremental stippling for sample distribution in spatially adaptive PIV image analysis
Adaptive incremental stippling for sample distribution in spatially adaptive PIV image analysis
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DOI：
10.1088/1361-6501/ab10b9
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发表时间：
2019-05
影响因子：
2.4
通讯作者：
Matthew Edwards;Raf Theunissen
中科院分区：
文献类型：
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作者：
Matthew Edwards;Raf Theunissen
PIV中的自适应采样策略已被证明可以有效地将有限的用户依赖性需求与在空间分辨率和计算工作量方面增加的性能相结合，从而使这些方法非常有趣。跨空间图像域的相关窗口的分配依赖于一个潜在的目标函数的解释，以及相应的窗口分布。重要的是，这种分配是计算效率高，鲁棒的变化的目标函数和条件，并有利于高质量的采样。本文提出了一种基于自适应增量点画的替代样本分布方法，并将基于pdf的方法的速度与“理想”弹簧力方法的质量相结合。不再需要依赖于实例的参数调优，从而提高了鲁棒性。此外，提出了一种自适应初始相关窗口大小的算法，以进一步降低用户依赖性。
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.