1 October 1991 Use of the generalized maximum likelihood algorithm for estimation of Markovian modeled image motion
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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.
Nader M. Namazi, Nader M. Namazi, David W. Foxall, 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 . Submission:
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