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Texture Analysis by Accurate Identification of Simple Markovian Models

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Cybernetics and Systems Analysis Aims and scope

Abstract

A more accurate identification (estimation of parameters) of simple Markov-Gibbs random field models of images results in a better segmentation of specific multimodal images and realistic synthesis of some types of natural textures. Identification algorithms for segmentation are based in part on a novel modification of an unsupervised learning algorithm published first in “Cybernetics and Systems Analysis” (“Kibernetika i Sistemnyi Analiz”) almost four decades ago. A texture synthesis algorithm uses an identified model for selecting a characteristic geometric shape of and a placement grid for texture elements sampled from a training image utilized for the identification.

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Authors and Affiliations

  1. University of Auckland, Auckland, New Zealand

    G. Gimel’farb

  2. University of Louisville, Kentucky, USA

    A. A. Farag

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  1. G. Gimel’farb
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  2. A. A. Farag
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Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 37–49, January–February 2005.

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Gimel’farb, G., Farag, A.A. Texture Analysis by Accurate Identification of Simple Markovian Models. Cybern Syst Anal 41, 27–38 (2005). https://doi.org/10.1007/s10559-005-0038-3

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  • DOI: https://doi.org/10.1007/s10559-005-0038-3

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