Three-dimensional predictive object tracking system

dc.contributor.advisorAgaian, Sos
dc.contributor.authorEzzati, Saeed
dc.contributor.committeeMemberAkopian, David
dc.contributor.committeeMemberKrishnan, Ram
dc.date.accessioned2024-02-09T21:11:24Z
dc.date.available2024-02-09T21:11:24Z
dc.date.issued2014
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractMany applications require tracking of complex 3D objects. These include visual navigating of robotic arms on specific target objects, Augmented Reality systems that require real-time registration of the object to be augmented, and head tracking systems that sophisticated interfaces can use. Computer Vision offers solutions that are cheap, practical and non-invasive. This thesis proposes a new method for creating a high quality 3D Models as a novel method for 3D predictive object tracking. The thesis contributes in two important ways to the research area of environmental models acquisition. First we introduced a new pre-processing step in obtaining a 3D model. This step is called Distance Optimization. During this step we calculate the best range for scanning a 3D object, using multiple cameras and single projector, based on the intrinsic and extrinsic parameters of the system. This process helps us to always locate the best scanning range for any kind of camera which benefits us in many ways such as scanning time and 3D quality. The second contribution to this research area, was to introduce a novel real time feedback process for improving the quality of the 3D model obtained by the system. During this step we combined two different 3D modeling system Stereo Matching and Structured Light, and used a novel wavelet fusion technique called Regional Based Wavelet Fusion, to create a more accurate depth map using our system. To extract more details from our 3D images, we also used a sharpening step along with a point-cloud merging algorithm. This new approach for creating a high quality 3D model will enable us to use our technique in a real-time 3-dimensional predictive object tracking system. The contributions presented in this thesis have been fully implemented and empirically evaluated by comparing to some of the newest approaches in this research area.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent131 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9781303919350
dc.identifier.urihttps://hdl.handle.net/20.500.12588/3529
dc.languageen
dc.subject3D
dc.subjectImage Processing
dc.subjectObject Tracking
dc.subjectDistane optimization
dc.subjectOptimization
dc.subjectPredictive
dc.subjectThree Dimensional
dc.subject.classificationElectrical engineering
dc.subject.classificationComputer engineering
dc.subject.classificationMathematics
dc.subject.lcshThree-dimensional imaging
dc.subject.lcshImage processing -- Digital techniques
dc.subject.lcshReal-time rendering (Computer graphics)
dc.titleThree-dimensional predictive object tracking system
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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