Estimation of dense image flow fields in fluids

The estimation of now fields from time sequences of satellite imagery has a number al 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, In this paper, we describe a method For the estimation of dense flow fields. Local measurements of motion are obtained bg analysis of the local energy distribution, which is sampled by using,a set of three-dimensional (3-D) spatio-temporal Biters, The estimated local energy distribution also allows us 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, we will constrain first-order derivatives of the Bow field, The performance of the algorithm is illustrated bg the estimation of the Bow fields corresponding to a sequence of Meteosat thermal images. The estimated Bow fields are used in a temporal interpolation scheme.