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dc.contributor.advisorKolsch, Mathias
dc.contributor.authorLotspeich, James T
dc.dateSep-12
dc.date.accessioned2012-11-14T00:02:47Z
dc.date.available2012-11-14T00:02:47Z
dc.date.issued2012-09
dc.identifier.urihttps://hdl.handle.net/10945/17407
dc.description.abstractIn many remote sensing applications, the area of a scene sensed by a single pixel can often be measured in square meters. This means that many objects of interest in a scene are smaller than a single pixel in the resulting image. Current tracking methods rely on robust object detection using multi-pixel features. A subpixel object does not provide enough information for these methods to work. This dissertation presents a method for tracking subpixel objects in image sequences captured from a stationary sensor that is critically sampled spatially. Using template matching, we estimate the maximum a posteriori probability of the target state over a sequence of images. A distance transform is used to calculate the motion prior in linear time, dramatically decreasing computation requirements. We compare the results of this method to a previously state-of-the-art track-before-detect particle filter designed for tracking small, low contrast objects using both synthetic and real-world imagery. Results show our method produces more accurate state estimates and higher detection rates than the current state of the art methods at signal-to-noise ratios as low as 3dB.en_US
dc.description.urihttp://archive.org/details/trackingsubpixel1094517407
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.titleTracking Subpixel Targets with Critically Sampled Optical Sensorsen_US
dc.typeThesisen_US
dc.contributor.departmentComputer Science
dc.subject.authorSubpixel, Tracking, Hidden Markov Model, Viterbi, Distance Transformen_US
dc.description.serviceMajor, United States Air Forceen_US
etd.thesisdegree.namePh.D in Computer Scienceen_US
etd.thesisdegree.levelDoctoralen_US
etd.thesisdegree.disciplineComputer Scienceen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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