一种实现自适应相关分析的变分方法用于粒子成像测速中运

In particle image velocimetry (PIV) a temporally separated
image pair of a gas or liquid seeded with small particles is recorded
and analysed in order to measure fluid flows therein. We investigate a
variational approach to cross-correlation, a robust and well-established
method to determine displacement vectors from the image data. A soft
Gaussian window function replaces the usual rectangular correlation
frame. We propose a criterion to adapt the window size and shape that
directly formulates the goal to minimise the displacement estimation error.
In order to measure motion and adapt the window shapes at the
same time we combine both sub-problems into a bi-level optimisation
problem and solve it via continuous multiscale methods. Experiments
with synthetic and real PIV data demonstrate the ability of our approach
to solve the formulated problem. Moreover window adaptation
yields significantly improved results. 自适应粒子成像测速场仪（PIV） Imager LX PIV相机 用于粒子成像测速(PIV)的荧光示踪粒子