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dc.contributor.advisorKeller, James M.eng
dc.contributor.advisorZare, Alinaeng
dc.contributor.authorOuadou, Aneseng
dc.date.issued2017eng
dc.date.submitted2017 Springeng
dc.description.abstractIn this thesis, we design and implement an algorithm for object detection in aerial images based on the morphological shared-weight neural network (MSNN). The multiple instance learning (MIL) framework is used to avoid the labeling problem required in a supervised learning framework. Using the MIL, each image was given a single label. We rely on the MSNN's ability to detect objects, and on the methodology used to generate bags to find our target. Two multiple-instance MSNN structures are developed. The performance of this framework is compared with the performance of a convolutional neural network (CNN) in the same condition.eng
dc.description.bibrefIncludes bibliographical references (103-107).eng
dc.description.statementofresponsibilityJames Keller, Thesis Supervisor.|Alina Zare, Thesis Co-supervisor.eng
dc.format.extent1 online resource (ix, 107 pages) : illustrations (chiefly color)eng
dc.identifier.merlinb121824664eng
dc.identifier.oclc1027221531eng
dc.identifier.urihttps://hdl.handle.net/10355/62071
dc.identifier.urihttps://doi.org/10.32469/10355/62071eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.eng
dc.sourceSubmited to University of Missouri--Columbia Graduate School.eng
dc.titleVehicle detection using morphological shared-weight neural network in the multiple instance learning frameworkeng
dc.typeThesiseng
thesis.degree.disciplineElectrical and computer engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


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