Use of the generalized maximum likelihood algorithm for estimation of Markovian modeled image motion

Nader M. Namazi; David W. Foxall

doi:10.1117/12.55968

1 October 1991 Use of the generalized maximum likelihood algorithm for estimation of Markovian modeled image motion

Nader M. Namazi, David W. Foxall

Author Affiliations +

Optical Engineering, Vol. 30, Issue 10, (October 1991). https://doi.org/10.1117/12.55968

Abstract

The generalized maximum likelihood (GML) algorithm is a gradient-based iterative algorithm for frame-to-frame motion estimation. This algorithm tends toward the maximum likelihood estimates of the Karhunen-Loève expansion coefficients of the motion field. The GML algorithm requires the covariance function matrix as a priori knowledge. Determination of the actual motion covariance in a practical situation is a difficult problem; the problem is approached by assuming that the motion vector is modeled by a separable stationary Markov-2 field. Using this model, we relate and compare the GML algorithm to another well-known motion estimator reported by Netravali and Robbins. Simulation experiments are presented that indicate the improvement of the GML algorithm over Netravali's scheme.

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Nader M. Namazi and David W. Foxall "Use of the generalized maximum likelihood algorithm for estimation of Markovian modeled image motion," Optical Engineering 30(10), (1 October 1991). https://doi.org/10.1117/12.55968

Published: 1 October 1991

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