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dc.contributor.advisorBall, John E.
dc.contributor.authorReza, Tasmia
dc.date2018
dc.date.accessioned2020-09-11T15:28:55Z
dc.date.available2020-09-11T15:28:55Z
dc.identifier.urihttps://hdl.handle.net/11668/19880
dc.description.abstractA comparison of performance between tradition support vector machine (SVM), single kernel, multiple kernel learning (MKL), and modern deep learning (DL) classifiers are observed in this thesis. The goal is to implement different machine-learning classification system for object detection of three dimensional (3D) Light Detection and Ranging (LiDAR) data. The linear SVM, non linear single kernel, and MKL requires hand crafted features for training and testing their algorithm. The DL approach learns the features itself and trains the algorithm. At the end of these studies, an assessment of all the different classification methods are shown.
dc.publisherMississippi State University
dc.subject.otherAdvanced Driver Assistance Systems
dc.subject.otherSupport Vector Machine
dc.subject.otherLiDAR
dc.subject.otherConvolutional Neural Networks
dc.titleObject Detection Using Feature Extraction and Deep Learning for Advanced Driver Assistance Systems
dc.typeThesis
dc.publisher.departmentDepartment of Electrical and Computer Engineering
dc.publisher.collegeBagley College of Engineering
dc.date.authorbirth1993
dc.subject.degreeMaster of Science
dc.contributor.committeeTang, Bo
dc.contributor.committeeAnderson, Derek T.


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