ПСЕВДОГРАДИЕНТНЫЙ АЛГОРИТМ ИДЕНТИФИКАЦИИ ДВАЖДЫ СТОХАСТИЧЕСКОГО ЦИЛИНДРИЧЕСКОГО ИЗОБРАЖЕНИЯ

Abstract

Nowadays image processing problems are becoming increasingly important due to development of the aerospace Earth monitoring systems, radio and sonar systems, medical devices for early diagnosis, etc. However, the most of the image processing works deals with images defined on rectangular two-dimensional grids or grids of higher dimension. In some practical situations images are set on a cylinder, for example images of pipeline sections, blood vessels, rotary parts, etc. The peculiarity of the domain for specifying such images requires its consideration in their models and processing algorithms. The article deals with autoregressive models of cylindrical images and gives some expressions of the correlation function depending on the autoregression parameters are given. To represent heterogeneous images with random heterogeneities, ‘doubly stochastic’ models are used in which one or more images control the parameters of resulted image. The spiral scan of a cylindrical image can be considered as a quasiperiodic process due to the correlation of image rows. The article proposes the pseudogradient algorithms for the modal identification. The statistical modeling proves these algorithms give good model identification.