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dc.contributor.advisorSteyn, W. H.en_ZA
dc.contributor.advisorHerbst, B. M.en_ZA
dc.contributor.authorMalan, Daniel Francoisen_ZA
dc.contributor.otherUniversity of Stellenbosch. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
dc.date.accessioned2009-05-22T09:13:48Zen_ZA
dc.date.accessioned2010-06-01T09:05:44Z
dc.date.available2009-05-22T09:13:48Zen_ZA
dc.date.available2010-06-01T09:05:44Z
dc.date.issued2005-03
dc.identifier.urihttp://hdl.handle.net/10019.1/3081
dc.descriptionThesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2005.
dc.description.abstractVisually estimating three-dimensional position, orientation and motion, between an observer and a target, is an important problem in computer vision. Solutions which compute threedimensional movement from two-dimensional intensity images, usually rely on stereoscopic vision. Some research has also been done in systems utilising a single (monocular) camera. This thesis investigates methods for estimating position and pose from monocular image sequences. The intended future application is of visual tracking between satellites flying in close formation. The ideas explored in this thesis build on methods developed for use in camera calibration, and structure from motion (SfM). All these methods rely heavily on the use of different variations of the Kalman Filter. After describing the problem from a mathematical perspective we develop different approaches to solving the estimation problem. The different approaches are successfully tested on simulated as well as real-world image sequences, and their performance analysed.en_ZA
dc.language.isoenen_ZA
dc.publisherStellenbosch : University of Stellenbosch
dc.subjectComputer visionen_ZA
dc.subjectKalman filteringen_ZA
dc.subjectTheses -- Electrical and electronic engineeringen_ZA
dc.subjectDissertations -- Electrical and electronic engineeringen_ZA
dc.subject.lcshKalman filteringen_ZA
dc.subject.lcshComputer visionen_ZA
dc.subject.otherElectrical and Electronic Engineeringen_ZA
dc.title3D tracking between satellites using monocular computer visionen_ZA
dc.typeThesisen_ZA
dc.rights.holderUniversity of Stellenbosch


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