On the Convergence of the Generalized Maximum Likelihood Algorithm for Nonuniform Image Motion Estimation
Document Type
Article
Publication Date
1-1-1992
Abstract
The generalized maximum likelihood algorithm is a powerful iterative scheme for waveform estimation. This algorithm seeks for the maximum likelihood estimates of the Karhunen-Loève expansion coefficients of the waveform. The search for the maximum is performed by the steepest ascent routine. The objective of this paper is to obtain conditions that assure the stability in the mean for frame-to-frame image motion estimation. Sufficient conditions are established for the convergence of the algorithm in the absence of noise. Experimental results are presented that illustrate the behavior of the algorithm in the presence of various noise levels. © 1992 IEEE
Publication Title
IEEE Transactions on Image Processing
Recommended Citation
Namazi, N.,
&
Foxall, D.
(1992).
On the Convergence of the Generalized Maximum Likelihood Algorithm for Nonuniform Image Motion Estimation.
IEEE Transactions on Image Processing,
1(1), 116-119.
http://doi.org/10.1109/83.128037
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/10263