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Advanced spatial information processes: modeling and application

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dc.contributor.authorZhang, Mingchuanen
dc.contributor.committeechairHaralick, Robert M.en
dc.contributor.committeememberEhrich, Roger W.en
dc.contributor.committeememberCampbell, James B. Jr.en
dc.contributor.committeememberYu, K.B.en
dc.contributor.committeememberRoach, John W.en
dc.contributor.departmentElectrical Engineeringen
dc.date.accessioned2017-03-10T15:15:10Zen
dc.date.available2017-03-10T15:15:10Zen
dc.date.issued1985en
dc.description.abstractMaking full use of spatial information is an important problem in information-processing and decision making. In this dissertation, two Bayesian decision theoretic frameworks for context classification are developed which make full use of spatial information. The first framework is a new multispectral image context classification technique which is based on a recursive algorithm for optimal estimation of the state of a two-dimensional discrete Markov Random Field (MRF). The implementation of the recursive algorithm is a form of dynamic programming. The second framework is based on a stochastic relaxation algorithm and Markov-Gibbs Random Fields. The relaxation algorithm constitutes an optimization using annealing. We also discuss how to estimate the Markov Random Field Model parameters, which is a key problem in using MRF in image processing and pattern recognition. The estimation of transition probabilities in a 2-D MRF is converted into two 1-D estimation problems. Then a Space-varying estimation method for transition probabilities is discussed.en
dc.description.degreePh. D.en
dc.format.extentv, 221 leavesen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/10919/76087en
dc.language.isoen_USen
dc.publisherVirginia Polytechnic Institute and State Universityen
dc.relation.isformatofOCLC# 16988467en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V856 1985.Z525en
dc.subject.lcshSpatial systemsen
dc.subject.lcshRandom fieldsen
dc.subject.lcshStochastic processesen
dc.titleAdvanced spatial information processes: modeling and applicationen
dc.typeDissertationen
dc.type.dcmitypeTexten
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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