A dynamic programming based matching method fur motion estimation that
optimists a Bayesian maximum likelihood function in a 3-D optimisatio
n space, is presented. The Bayesian function consists of a matching co
st and an object based 2-D regularisation cost. The method gives resul
ts more accurate than block-based matching since the motion boundaries
are close to the actual object boundaries.