Texture analysis and synthesis using a generic Markov-Gibbs image model

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dc.contributor.advisor Gimel'farb, Georgy
dc.contributor.author Zhou, Dongxiao en
dc.date.accessioned 2008-12-16T22:49:20Z en
dc.date.available 2008-12-16T22:49:20Z en
dc.date.issued 2006 en
dc.identifier.uri http://hdl.handle.net/2292/3280 en
dc.description Full text is available to authenticated members of The University of Auckland only. en
dc.description.abstract Texture analysis and synthesis have become very important research subjects in digital image processing and computer vision. The primary focus of this thesis is on developing a structural identification of a generic Markov-Gibbs random field model of textures which results in a new method for fast texture analysis and synthesis. Probability models, in particular Markov-Gibbs random fields (MGRF) have gained wide acceptance for solving applied image recognition, analysis, and synthesis problems. An MGRF model of textures is usually specified by a Gibbs probability distribution on selected texture features such as image signal statistics. Despite of modelling power and expressiveness of an MGRF model, the traditional identification of the model, i.e. estimating model parameters for a particular image, is a computationally complex process, because it usually involves Markov Chain Monte Carlo (MCMC) algorithms with an exponential time complexity. en
dc.publisher ResearchSpace@Auckland en
dc.relation.ispartof PhD Thesis - University of Auckland
dc.relation.isreferencedby UoA99168231414002091 en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.title Texture analysis and synthesis using a generic Markov-Gibbs image model en
dc.type Thesis en
thesis.degree.discipline Computer Science
thesis.degree.grantor The University of Auckland en
thesis.degree.level Doctoral en
thesis.degree.name PhD
dc.rights.holder Copyright: The author

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