Effectiveness of correlation and information measures for synthesis of recurrent algorithms for estimating spatial deformations of video sequences

Abstract

A comparative analysis of the efficiency of correlation (cross-correlation, Tanimoto coefficient and Kendall's rank correlation coefficient) and information (mutual information of Tsallis and Shannon, F-information measure and entropy of the joint probability distribution) measures of image similarity for the synthesis of recursive estimation algorithms is presented for the problem of estimating parameters of spatial deformations of a sequence of images. Unbiased additive Gaussian noise was used as an interfering factor in the experimental studies. It is shown that the potentially high convergence rate of the estimated parameters and the smaller variance of the estimation error from the investigated correlation measures are ensured by the Tonimoto coefficient, and from the I-information of the F-information among the information measures. According to these criteria, the Kendall's rank correlation coefficient and the M-measure of F-information are inferior, respectively.