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Bayesian Estimation of Motion Vector Fields
September 1992 (vol. 14 no. 9)
pp. 910-927

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.

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Index Terms:
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
Citation:
J. Konrad, E. Dubois, "Bayesian Estimation of Motion Vector Fields," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 9, pp. 910-927, Sept. 1992, doi:10.1109/34.161350
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