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.
Several approaches to the numerical description of image inter-frame geometric deformations parameters estimates behavior at iterations of non-identification relay stochastic gradient estimation are considered. The probability density of the Euclidean mismatch distance of estimates vector is chosen as an argument of the characteristics forming the numerical values. It made it possible to ensure invariance of research to the set of parameters of the used inter-frame geometric deformations model. The mathematical expectation, the probability of exceeding a given threshold value of the convergence rate and the confidence interval of the Euclidean mismatch distance were investigated as characteristics. Probabilistic mathematical modeling is applied to calculate the probability density of the Euclidean mismatch distance.
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