Variable threshold outlier identification in PIV data

This paper describes a variable threshold technique that can be applied to any particle image velocimetry (PIV) post-analysis outlier identification algorithm which uses a threshold such as the local median or the cellular neural network techniques. Although these techniques have been shown to work quite well with constant thresholds, the selection of the threshold is not always clear when working with real data. Moreover, if a small threshold is selected, a very large number of valid vectors can be mistakenly rejected. Although careful monitoring may alleviate this danger in many cases, that is not always practical when large data sets are being analysed and there is significant variability in the properties of the vector fields. The method described in this paper adjusts the threshold by calculating a mean variation between a candidate vector and its eight neighbours. The main benefit is that much smaller thresholds can be used without suffering catastrophic loss of valid vectors. The main challenge in obtaining this threshold field is that it must be based on a filtered field to be representative of the underlying velocity field. In this work, a simple median filter which requires no threshold was used for preliminary rejection. A local threshold was then calculated from the mean difference between each vector and its neighbours. The threshold field was also filtered with a Gaussian kernel before use. The algorithm was tested and compared to the base techniques by generating artificial velocity fields with known numbers of spurious vectors. For these tests, the ability of the algorithms to identify bad vectors and preserve good vectors was monitored. In addition, the technique was tested on real PIV data from the developing region of an axisymmetric jet. The variable threshold versions of these algorithms were found to be much less susceptible to erroneously rejecting good vectors. This is because the variable threshold techniques extract information about the local velocity gradient from the data themselves. The user-adjustable parameters for the variable threshold methods were found to be more universal than the constant threshold methods.