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Optical Engineering

Use of the generalized maximum likelihood algorithm for estimation of Markovian modeled image motion
Author(s): Nader M. Namazi; David W. Foxall
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Paper 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.

Paper Details

Date Published: 1 October 1991
PDF: 4 pages
Opt. Eng. 30(10) doi: 10.1117/12.55968
Published in: Optical Engineering Volume 30, Issue 10
Show Author Affiliations
Nader M. Namazi, Michigan Technological Univ. (United States)
David W. Foxall, Michigan Technological Univ. (United States)


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