Estimation of complex air-water interfaces from particle image velocimetry images

This paper describes a method for the estimation of the instantaneous air–water interface directly from particle image velocimetry (PIV) images of a laboratory generated air entraining turbulent hydraulic jump. Image processing methods such as texture segmentation based on gray level co-occurrence matrices are used to obtain a first approximation for the discrete location of the free surface. Active contours based on energy minimization principles are then implemented to get a more accurate estimate of the calculated interface and draw it closer to the real surface. Results are presented for two sets of images with varying degrees of image information and surface deformation. Comparisons with visually-interpreted surfaces show good agreement. In the absence of in-situ measurements, several verification tests based on physical reasoning show that the free surface is calculated to acceptable levels of accuracy. Aside from a single image used to tune the set of parameters, the algorithm is completely automated to process an ensemble of images representative of typical PIV applications. The method is computationally efficient and can be used to track fluid-interfaces undergoing non-rigid deformations.