Estimation of 2-D Motion Fields from Image Sequences with Application to Motion-Compensated Processing

In this chapter we are concerned with the estimation of 2-D motion from time-varying images and with the application of the computed motion to image sequence processing. Our goal for motion estimation is to propose a general formulation that incorporates object acceleration, nonlinear motion trajectories, occlusion effects and multichannel (vector) observations. To achieve this objective we use Gibbs-Markov models linked together by the Maximum A Posteriori Probability criterion which results in minimization of a multiple-term cost function. The specific applications of motion-compensated processing of image sequences are prediction, noise reduction and spatiotemporal interpolation.

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