The identification of doubly stochastic circular image model

• Published in: Procedia computer science Vol. 176; pp. 1839 - 1847
• Main Authors: Krasheninnikov, Victor, Malenova, Olga, Subbotin, Alexey
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2020
• Subjects: circular image; doubly stochastic models; model idenification; pseudogradient algorithms; doubly stochastic models; pseudogradient algorithms; model idenification; circular image
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2020.09.223

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.