On the convergence of the generalized maximum likelihood algorithm for nonuniform image motion estimation

The generalized maximum likelihood algorithm is a powerful iterative scheme for waveform estimation. This algorithm seeks for the maximum likelihood estimates of the Karhunen-Loeve expansion coefficients of the waveform. The search for the maximum is performed by the steepest ascent routine. The objective of the 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