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dc.contributor.advisorKolsch, Mathias
dc.contributor.authorCrisman, Donald M.
dc.dateSep-16
dc.date.accessioned2016-11-02T17:18:23Z
dc.date.available2016-11-02T17:18:23Z
dc.date.issued2016-09
dc.identifier.urihttp://hdl.handle.net/10945/50527
dc.description.abstractIn the last few decades, machine learning and computer vision techniques have enabled precise and repeatable image recognition. Computer vision techniques can also recognize star patterns in star trackers for satellite attitude determination. Horizon detection in the visible spectrum was largely discarded for attitude determination in favor of thermal imagery, due to the greater consistency of the earth's thermal radiation. This thesis examines computer vision and machine learning techniques to develop a horizon detection algorithm for the visible spectrum. By examining different features of visual imagery, machine learning techniques were evaluated on the ability to detect a visible horizon and determine its orientation. An empirical analysis of visual imagery from low-earth orbit was conducted to develop a horizon brightness transition model, which allows for consistent and adjustable determination of the horizons location. The final result is a horizon detection and orientation determination algorithm that successfully indicates if a horizon is present in an image with 96% precision and 92% recall. The brightness model correctly identifies the location of the horizon in 85% of the tested image set.en_US
dc.description.urihttp://archive.org/details/horizondetection1094550527
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleHorizon detection in the visible spectrumen_US
dc.typeThesisen_US
dc.contributor.secondreaderBursch, Daniel
dc.contributor.departmentComputer Science
dc.subject.authorattitude determinationen_US
dc.subject.authormachine learningen_US
dc.subject.authorimage classificationen_US
dc.subject.authorearth horizon sensoren_US
dc.subject.authorcomputer visionen_US
dc.subject.authorline detectionen_US
dc.subject.authorvisible horizonen_US
dc.subject.authorvisible spectrum imageryen_US
dc.subject.authorhorizon detectionen_US
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameMaster of Science in Computer Scienceen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineComputer Scienceen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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