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dc.contributor.authorLang, Yanda
dc.date.accessioned2017-11-06T23:49:36Z
dc.date.available2017-11-06T23:49:36Z
dc.date.issued2017-05
dc.identifier.urihttp://hdl.handle.net/11122/7968
dc.descriptionMaster's Project (M.S.) University of Alaska Fairbanks, 2017en_US
dc.description.abstractThis project describes a method for edge detection in images. We develop a Bayesian approach for edge detection, using a process convolution model. Our method has some advantages over the classical edge detector, Sobel operator. In particular, our Bayesian spatial detector works well for rich, but noisy, photos. We first demonstrate our approach with a small simulation study, then with a richer photograph. Finally, we show that the Bayesian edge detector performance gives considerable improvement over the Sobel operator performance for rich photos.en_US
dc.language.isoen_USen_US
dc.subjectImage processingen_US
dc.subjectDigital techniquesen_US
dc.subjectBayesian statistical decision theoryen_US
dc.titleEdge detection using Bayesian process convolutionsen_US
dc.typeOtheren_US
dc.type.degreems
dc.identifier.departmentDepartment of Mathematics and Statistics
dc.contributor.chairShort, Margaret
dc.contributor.committeeBarry, Ron
dc.contributor.committeeGoddard, Scott
dc.contributor.committeeMcIntyre, Julie
refterms.dateFOA2020-03-05T15:04:44Z


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