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dc.contributor.advisorHe, Zhihai, 1973-eng
dc.contributor.authorLi, Xin, 1984-eng
dc.date.issued2010eng
dc.date.submitted2010 Falleng
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on March 30, 2011).eng
dc.descriptionThe entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.eng
dc.descriptionThesis advisor: Dr. Zhihai He.eng
dc.descriptionIncludes bibliographical references.eng
dc.descriptionM.S. University of Missouri--Columbia 2010.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Computer engineering.eng
dc.description.abstractIn this work, we develop a license plate detection and recognition method using a SVM (Support Vector Machine) classifier with HOG (Histogram of Oriented Gradients) features. The system performs window searching at different scales and analyzes the HOG feature using a SVM and locates their bounding boxes using a Mean Shift method. A car head and rear detection method was also proposed to accelerate the time consuming scanning process. A comparison of the performance for different cell and block sizes of HOG feature is provided, and four rounds of bootstrapping was performed to achieve better detection performance. Our license plate detection results show that this method is relatively insensitive to variations in illumination, license plate patterns, camera perspective and background variations. We tested our method on the Caltech data set (1999), and achieved a detection rate of 96.0%. We also studied how its performance is impacted by different levels of noise and motion blur. After license plate detection, we proceed to perform character segmentation and recognition using SVM classifiers with HOG features. In character segmentation, we need to deal with low contrast and tilted plates. The system performs window searching in different scales and analyzes the HOG feature using a SVM and locates their bounding boxes using Mean Shift.eng
dc.format.extentvii, 61 pageseng
dc.identifier.merlinb82191918eng
dc.identifier.oclc711898125eng
dc.identifier.otherLiX-120910-T789eng
dc.identifier.urihttp://hdl.handle.net/10355/10564eng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartof2010 Freely available theses (MU)eng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Theses. 2010 Theseseng
dc.subject.lcshAutomobile license plateseng
dc.subject.lcshVehicle detectorseng
dc.subject.lcshSupport vector machineseng
dc.subject.lcshMotor vehicles -- Automatic location systemseng
dc.titleVehicle license plate detection and recognitioneng
dc.typeThesiseng
thesis.degree.disciplineComputer engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


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