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dc.contributor.advisorHe, Zhihai, 1973-en_US
dc.contributor.authorLi, Xin, 1984-en_US
dc.date.issued2010en_US
dc.date.submitted2010 Fallen_US
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on March 30, 2011).en_US
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.en_US
dc.descriptionThesis advisor: Dr. Zhihai He.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.descriptionM.S. University of Missouri--Columbia 2010.en_US
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Computer engineering.en_US
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.en_US
dc.format.extentvii, 61 pagesen_US
dc.identifier.merlinb82191918
dc.identifier.oclc711898125en_US
dc.identifier.otherLiX-120910-T789en_US
dc.identifier.urihttp://hdl.handle.net/10355/10564
dc.publisherUniversity of Missouri--Columbiaen_US
dc.relation.ispartof2010 Freely available theses (MU)en_US
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Theses. 2010 Theses
dc.subject.lcshAutomobile license platesen_US
dc.subject.lcshVehicle detectorsen_US
dc.subject.lcshSupport vector machinesen_US
dc.subject.lcshMotor vehicles -- Automatic location systemsen_US
dc.titleVehicle license plate detection and recognitionen_US
dc.typeThesisen_US
thesis.degree.disciplineComputer engineeringen_US
thesis.degree.grantorUniversity of Missouri--Columbiaen_US
thesis.degree.levelMastersen_US
thesis.degree.nameM.S.en_US


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