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dc.contributor.advisorHo, Dominic K. C.eng
dc.contributor.authorHarris, Samuel D.eng
dc.date.issued2016eng
dc.date.submitted2016 Springeng
dc.description.abstract[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Landmines and other buried explosives endanger military and civilian lives around the world. The detection of these types of subsurface objects using ground penetrating radar (GPR) is a difficult problem, partially due to variation in the size, content, and burial depth of different targets. Hand-held object detection systems pose yet another challenge, due to the inescapable human operator effect. In this thesis, computational methods for detecting dangerous targets in GPR data are explored. First, an anomaly detection algorithm which can act as a prescreener is described. Then, two feature extraction methods are implemented in order to extract relevant features from the GPR data at each prescreener alarm location. The first feature set describes hyperbolic edge features in the data by applying 36 log-Gabor filters to each B-scan and summing the energy over the filtered GPR data. The second feature extraction method collects local texture information from each B-scan by computing local binary patterns (LBP). Support vector machines (SVM) are trained to perform target vs. non-target classification using the different feature sets. Confidence values are then extracted from the trained SVMs and coupled with the prescreener confidence values in order to improve the overall detection performance.eng
dc.description.bibrefIncludes bibliographical references.eng
dc.format.extent1 online resource (x, 92 pages) : illustrationseng
dc.identifier.urihttps://hdl.handle.net/10355/60441
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsAccess is limited to the campuses of the University of Missouri.eng
dc.titleSupervised learning methods for hand-held ground penetrating radareng
dc.typeThesiseng
thesis.degree.disciplineComputer science (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


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