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dc.contributor.authorLytle, Alan Marshallen_US
dc.date.accessioned2014-03-14T20:09:42Z
dc.date.available2014-03-14T20:09:42Z
dc.date.issued2011-04-01en_US
dc.identifier.otheretd-04152011-111750en_US
dc.identifier.urihttp://hdl.handle.net/10919/26888
dc.description.abstractObject recognition systems require baseline information upon which to compare sensed data to enable a recognition task. The ability to integrate a diverse set of object recognition data for different components in a Building Information Model (BIM) will enable many autonomous systems to access and use these data in an on-demand learning capacity, and will accelerate the integration of object recognition systems in the construction environment. This research presents a new framework for linking feature descriptors to a BIM to support construction object recognition. The proposed framework is based upon the Property and External Reference Resource schemas within the IFC 2x3 TC1 architecture. Within this framework a new Property Set (Pset_ObjectRecognition) is suggested which provides an on-demand capability to access available feature descriptor information either embedded in the IFC model or referenced in an external model database. The Property Set is extensible, and can be modified and adjusted as required for future research and field implementation. With this framework multiple sets of feature descriptors associated with different sensing modalities and different algorithms can all be aggregated into one Property Set and assigned to either object types or object instances.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartLytle_AM_D_2011.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectBuilding Information Modelingen_US
dc.subjectBIMen_US
dc.subjectIndustry Foundation Classesen_US
dc.subjectObject Recognitionen_US
dc.subject3D Imagingen_US
dc.subjectIFCen_US
dc.titleA Framework for Object Recognition in Construction Using Building Information Modeling and High Frame Rate 3D Imagingen_US
dc.typeDissertationen_US
dc.contributor.departmentCivil Engineeringen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineCivil Engineeringen_US
dc.contributor.committeechairSinha, Sunil Kumaren_US
dc.contributor.committeememberBeliveau, Yvan J.en_US
dc.contributor.committeememberBulbul, Tanyelen_US
dc.contributor.committeememberGolparvar-Fard, Manien_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-04152011-111750/en_US
dc.date.sdate2011-04-15en_US
dc.date.rdate2011-04-25
dc.date.adate2011-04-25en_US


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