The identification of doubly stochastic circular image model

In some practical situations, images are set on a circle. For example, images of the facies (thin film) of dried biological fluid, eyes, cut of a tree trunk, etc. Currently, most of the image processing works deal with images defined on rectangular two-dimensional grids or grids of higher dimension....

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Bibliographic Details
Published in:Procedia computer science 2020, Vol.176, p.1839-1847
Main Authors: Krasheninnikov, Victor, Malenova, Olga, Subbotin, Alexey
Format: Article
Language:English
Subjects:
circular image
doubly stochastic models
model idenification
pseudogradient algorithms
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Summary:In some practical situations, images are set on a circle. For example, images of the facies (thin film) of dried biological fluid, eyes, cut of a tree trunk, etc. Currently, most of the image processing works deal with images defined on rectangular two-dimensional grids or grids of higher dimension. The features of circle images require their consideration in their mathematical models. In this paper, an autoregressive models of homogeneous and inhomogeneous random fields defined on a circle are considered as representations of images with radial or radial-circular structure. In the present paper, autoregressive models of circular images are considered. To represent heterogeneous images with random heterogeneities, «doubly stochastic» models are used in which one or more images control the parameters of the resulting image. Pseudo-gradient algorithms for the modal identification are proposed. The conducted statistical modeling showed that these algorithms give good model identification.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2020.09.223