Issue No. 09 - September (1992 vol. 14)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.161350
<p>A stochastic approach to the estimation of 2D motion vector fields from time-varying images is presented. The formulation involves the specification of a deterministic structural model along with stochastic observation and motion field models. Two motion models are proposed: a globally smooth model based on vector Markov random fields and a piecewise smooth model derived from coupled vector-binary Markov random fields. Two estimation criteria are studied. In the maximum a posteriori probability (MAP) estimation, the a posteriori probability of motion given data is maximized, whereas in the minimum expected cost (MEC) estimation, the expectation of a certain cost function is minimized. Both algorithms generate sample fields by means of stochastic relaxation implemented via the Gibbs sampler. Two versions are developed: one for a discrete state space and the other for a continuous state space. The MAP estimation is incorporated into a hierarchical environment to deal efficiently with large displacements.</p>
Bayesian estimation; minimum expected cost estimation; picture processing; 2D motion vector fields; time-varying images; deterministic structural model; vector Markov random fields; piecewise smooth model; maximum a posteriori probability; stochastic relaxation; Gibbs sampler; state space; Bayes methods; estimation theory; Markov processes; picture processing; probability; state-space methods
E. Dubois and J. Konrad, "Bayesian Estimation of Motion Vector Fields," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 14, no. , pp. 910-927, 1992.