Computational Expenditure Reduction in Pseudo-Gradient Image Parameter Estimation

Abstract

An approach enabling to reduce computational expenses at pseudogradient estimation of image parameters based on control of goal function local sample volume is proposed. Local sample volume variation during the process of parameter estimation occurs automatically in correspondence with a preassigned criterion, for instance, sample correlation coefficient. It is shown that for the problems of image mutual spatial deformation parameter estimation, computational expenses can be reduced several times as many.

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

“…A variant of the solution of this problem was considered in [7,8]. In this work several correlation correlations have been selected for the study: cross-correlation (the coefficient of interframe correlation) [9], the Tanimoto coefficient [10] the Kendall's rank correlation coefficient [11], and a number of informational SMs: Tsallis [12] and Shannon mutual information, F-information measures, and the entropy of the joint probability distribution [13]. Unbiased additive Gaussian noise was used as an interfering factor in the studies.…”

“…Shannon mutual information (MI) is one of the most widely used similarity measure in image registration [12,13] as it provides an extremely high accuracy when images have linear and non-linear intensity distortions, occlusions and also in case of additive noise and multimodal images. Generalized Shannon MI can be defined in terms of entropy as:…”

An algorithm for detection of radio pulses received by spatially distributed sensors

“…The algorithm for estimating the parameters of mutual mismatches between signals can be used to estimate the time shift of unfiltered signals from different antenna array elements [12]. The algorithm is based on stochastic gradient ascent.…”

Optimization of Goal Function Pseudogradient in the Problem of Interframe Geometrical Deformations Estimation

“…The formed estimates are immune to pulse interferences and converge to true values under rather weak conditions. The processing of the image samples can be performed in an arbitrary order, for example, in order of scanning with decimation that is determined by the hardware speed, which facilitates obtaining a tradeoff between image entering rate and the speed of the available hardware (Tashlinskii, 2003). However, pseudogradient procedures have disadvantages, in particular, the presence of local extremums of the goal function estimate at real image processing, that significantly reduces the convergence rate of parameters estimates.…”