Analytic Approximation of Gibbs Potentials to Model Stochastic Textures

Show simple item record Gimel'farb, Georgy en 2009-04-08T04:03:16Z en 2009-04-08T04:03:16Z en 1997-10 en
dc.identifier.citation Computer Science Technical Reports 158 (1997) en
dc.identifier.issn 1173-3500 en
dc.identifier.uri en
dc.description.abstract Gibbs random fields with multiple pairwise pixel interactionshave good potentialities in modeling natural image texturesbecause allow for learning both the structure and strengths ofpixel interactions from a given training sample. The learningscheme is based on the maximum likelihood estimate (MLE) ofGibbs potentials that specify the interaction strenghts. Thisscheme is amplified here by deducing an explicit, to scalingfactors, analytic form of the potentials from an additionalfeasible top rank principle. It suggests that the training samplemay possess a feasible top rank in its total Gibbs energy withinthe parent population. Under this condition, only the scaling factorshave to be learnt using their MLE. As a result, the introducedconditional MLE of the potentials extends capabilities of the Gibbsimage models under consideration. en
dc.publisher Department of Computer Science, The University of Auckland, New Zealand en
dc.relation.ispartofseries Computer Science Technical Reports en
dc.rights.uri en
dc.source.uri en
dc.title Analytic Approximation of Gibbs Potentials to Model Stochastic Textures en
dc.type Technical Report en
dc.subject.marsden Fields of Research::280000 Information, Computing and Communication Sciences en
dc.rights.holder The author(s) en

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