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dc.contributor.advisorFouts, Douglas
dc.contributor.advisorKolsch, Mathias
dc.contributor.authorCamp, David M.
dc.dateDec-13
dc.date.accessioned2014-02-18T23:38:40Z
dc.date.available2014-02-18T23:38:40Z
dc.date.issued2013-12
dc.identifier.urihttp://hdl.handle.net/10945/38891
dc.descriptionApproved for public release; distribution is unlimited.en_US
dc.description.abstractThe research described here examined computer vision algorithms for suitability to aid or replace the current methods of ship detection and tracking from a photonics mast. Evaluation was conducted on three object detection methods: a bag of words (BOW) robust multi-class classification method; a histogram of oriented gradient (HOG) method, originally used for pedestrian tracking; and a deformable parts model (DPM) that was originally designed for pose recognition that has been successful in multi-class classification. A fourth method that combines the HOG and BOW was created and successfully reduced false positive detections while maintaining a high recall rate. The object detection methods were evaluated through a search theory model to frame evaluation for operational ship detection. Each object detection method was optimized following a design of experiments approach utilizing a cluster computer. The BOW method had the highest recall for ships 25 pixels and smaller, while the HOG method was the fastest of all methods when implemented on a graphical processing unit. The DPM method had the highest average recall for ships greater than 25 pixels but the lowest recall for smaller ships. Finally, the hybrid HOG and BOW method had the highest mean recall and lowest mean false positive rate over all ship sizes.en_US
dc.description.urihttp://archive.org/details/evaluationofobje1094538891
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. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.titleEvaluation of object detection algorithms for ship detection in the visible spectrumen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical And Computer Engineering
dc.subject.authorShip detectionen_US
dc.subject.authorsearch theoryen_US
dc.subject.authorcomputer visionen_US
dc.subject.authorcluster computeren_US
dc.subject.authorgraphical processor unit (GPU)en_US
dc.subject.authorparallel computingen_US
dc.subject.authordesign of experimentsen_US
dc.subject.authorobject detectionen_US
dc.description.recognitionOutstanding Thesisen_US
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameMaster Of Science In Electrical Engineeringen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineElectrical Engineeringen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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