Motion estimation from noisy image data

The problem of motion estimation is formalized as a problem in nonlinear optimization. The algorithm is based on modeling the displacement fields as Markov random fields. The Markov random fields-Gibbs distribution equivalence is used to convert the problem into one of finding an appropriate energy function that describes the motion fields. Mean field annealing, a technique for finding the global minima in nonconvex optimization problems, is used to minimize the Hamiltonian. The estimated displacement vector fields are accurate, even for scenes containing noise of intensity discontinuities.<>