Goleshchikhin

Bio: Goleshchikhin is an academic researcher. The author has contributed to research in topics: Image segmentation & Iterative reconstruction. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

A multilevel Gibbs model for texture image analysis

Abstract: A texture image model based on Gibbs distribution is suggested. The model contains three levels - one observable (pixel) level corresponding to the texture image and two unobservable (hidden) levels. One hidden level represents a map of texture (i.e. a field of texture domain labels) and the other consists of contour elements and conforms to line process. The model is meant for texture image segmentation and analysis. Some results of experiments are presented.

Hierarchical Gibbs models for image processing

Abstract: Hierarchical models of images have been used in image processing rather widely because of evidently hierarchical nature of human vision Gibbs distribution due to its universality looks like the most convenient formalism for making use of those models In the paper an approach to image processing and image analysis based on hierarchical beamlet structure associated with Gibbs distribution is offered