Estimation of dense image flow fields in fluids

The estimation of flow fields from time sequences of satellite imagery has a number of important applications. For visualization of cloud or sea ice movements in sequences of crude temporal sampling, a satisfactory nonblurred temporal interpolation can be performed only when the flow field or an estimate thereof is known. Estimated flow fields in weather satellite imagery might also be used on an operational basis as inputs to short-term weather prediction. The authors describe a method for the estimation of dense flow fields. Local measurements of motion are obtained by analysis of the local energy distribution, which is sampled by using a set of three-dimensional (3D) spatio-temporal filters. The estimated local energy distribution also allows the authors to compute a confidence measure of the estimated local normal flow. The algorithm, furthermore, utilizes Markovian random fields in order to integrate the local estimates of normal flows into a dense flow field by using measures of spatial smoothness. To obtain smoothness, the authors will constrain first-order derivatives of the flow field. The performance of the algorithm is illustrated by the estimation of the flow fields corresponding to a sequence of Meteosat thermal images. The estimated flow fields are used in a temporal interpolation scheme.