Markov random field image modelling

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dc.contributor.advisorDe Jager, Gerharden_ZA
dc.contributor.authorMcGrath, Michaelen_ZA
dc.date.accessioned2014-07-31T10:54:49Z
dc.date.available2014-07-31T10:54:49Z
dc.date.issued2003en_ZA
dc.descriptionIncludes bibliographical references.
dc.description.abstractThis work investigated some of the consequences of using a priori information in image processing using computer tomography (CT) as an example. Prior information is information about the solution that is known apart from measurement data. This information can be represented as a probability distribution. In order to define a probability density distribution in high dimensional problems like those found in image processing it becomes necessary to adopt some form of parametric model for the distribution. Markov random fields (MRFs) provide just such a vehicle for modelling the a priori distribution of labels found in images. In particular, this work investigated the suitability of MRF models for modelling a priori information about the distribution of attenuation coefficients found in CT scans.en_ZA
dc.identifier.apacitationMcGrath, M. (2003). <i>Markov random field image modelling</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/5166en_ZA
dc.identifier.chicagocitationMcGrath, Michael. <i>"Markov random field image modelling."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2003. http://hdl.handle.net/11427/5166en_ZA
dc.identifier.citationMcGrath, M. 2003. Markov random field image modelling. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - McGrath, Michael AB - This work investigated some of the consequences of using a priori information in image processing using computer tomography (CT) as an example. Prior information is information about the solution that is known apart from measurement data. This information can be represented as a probability distribution. In order to define a probability density distribution in high dimensional problems like those found in image processing it becomes necessary to adopt some form of parametric model for the distribution. Markov random fields (MRFs) provide just such a vehicle for modelling the a priori distribution of labels found in images. In particular, this work investigated the suitability of MRF models for modelling a priori information about the distribution of attenuation coefficients found in CT scans. DA - 2003 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2003 T1 - Markov random field image modelling TI - Markov random field image modelling UR - http://hdl.handle.net/11427/5166 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/5166
dc.identifier.vancouvercitationMcGrath M. Markov random field image modelling. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2003 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/5166en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleMarkov random field image modellingen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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