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At stochastic gradient estimation of image parameters the estimates convergence character and computational expenses essentially depend on image samples local sample size used for obtaining the stochastic gradient. In the paper the possibility of a priori optimization of the volume of a local sample to minimize computational costs at geometrical images deformations estimation is considered. The minimum of the given computational costs for the conventional unit of expectation of the improvement of the evaluation is chosen as an optimization criterion. The block diagram of one of the algorithms and the examples of calculation results are presented.

Reduction of the computational complexity of stochastic gradient algorithms of image parameters estimation for a priori optimization of the local sample volume