A robust variable order facet model for image data

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dc.contributor.authorMainguy, Yvesen
dc.contributor.committeecochairWatson, Layne T.en
dc.contributor.committeecochairBirch, Jeffrey B.en
dc.contributor.committeememberEhrich, Roger W.en
dc.contributor.departmentComputer Science and Applicationsen
dc.date.accessioned2014-03-14T21:47:58Zen
dc.date.adate2009-10-22en
dc.date.available2014-03-14T21:47:58Zen
dc.date.issued1991-12-05en
dc.date.rdate2009-10-22en
dc.date.sdate2009-10-22en
dc.description.abstractThe underlying piecewise continuous surface of a digital image can be estimated through robust statistical procedures. This thesis contains a systematic Monte Carlo study of M-estimation and LMS estimation for image surface approximation, an examination of the merits of postprocessing and tuning various parameters in the robust estimation procedures, and a new robust variable order facet model paradigm. Several new goodness of fit measures are introduced, and systematically compared. It is shown that the M-estimation tuning parameters are not crucial, postprocessing is cheap and well worth the cost, and the robust variable order facet model algorithm (using M-estimation, new statistical goodness of fit measures, and postprocessing) manages to retain most of the statistical efficiency of Mestimation yet displays good robustness properties, and preserves the main geometric features of an image surface: step edges, roof edges and corners.en
dc.description.degreeMaster of Scienceen
dc.format.extent65 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-10222009-124949en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-10222009-124949/en
dc.identifier.urihttp://hdl.handle.net/10919/45239en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V855_1991.M358.pdfen
dc.relation.isformatofOCLC# 25404410en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V855 1991.M358en
dc.subject.lcshComputer vision -- Statistical methodsen
dc.subject.lcshImage processing -- Digital techniques -- Statistical methodsen
dc.titleA robust variable order facet model for image dataen
dc.typeThesisen
dc.type.dcmitypeTexten
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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