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dc.contributor.authorIoannou, Yani Andrew
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2010-03-01 12:26:34.698en
dc.date.accessioned2010-03-01T22:09:37Z
dc.date.available2010-03-01T22:09:37Z
dc.date.issued2010-03-01T22:09:37Z
dc.identifier.urihttp://hdl.handle.net/1974/5443
dc.descriptionThesis (Master, Computing) -- Queen's University, 2010-03-01 12:26:34.698en
dc.description.abstractRecent advances in Light Detection and Ranging (LIDAR) technology and integration have resulted in vehicle-borne platforms for urban LIDAR scanning, such as Terrapoint Inc.'s TITAN system. Such technology has lead to an explosion in ground LIDAR data. The large size of such mobile urban LIDAR data sets, and the ease at which they may now be collected, has shifted the bottleneck of creating abstract urban models for Geographical Information Systems (GIS) from data collection to data processing. While turning such data into useful models has traditionally relied on human analysis, this is no longer practical. This thesis outlines a methodology for automatically recovering the necessary information to create abstract urban models from mobile urban LIDAR data using computer vision methods. As an integral part of the methodology, a novel scale-based interest operator is introduced (Di erence of Normals) that is e cient enough to process large datasets, while accurately isolating objects of interest in the scene according to real-world parameters. Finally a novel localized object recognition algorithm is introduced (Local Potential Well Space Embedding), derived from a proven global method for object recognition (Potential Well Space Embedding). The object recognition phase of our methodology is discussed with these two algorithms as a focus.en
dc.languageenen
dc.language.isoenen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectcomputer visionen
dc.subjectcomputer scienceen
dc.subjectLIDARen
dc.subjectLADARen
dc.subjectobject recognitionen
dc.subjectsegmentationen
dc.subjectpoint clouden
dc.subjectrange dataen
dc.subjectinterest operatoren
dc.subjectscale theoryen
dc.titleAutomatic Urban Modelling using Mobile Urban LIDAR Dataen
dc.typethesisen
dc.description.degreeMasteren
dc.contributor.supervisorGreenspan, Michael A.en
dc.contributor.supervisorHarrap, Robinen
dc.contributor.departmentComputingen


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