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dc.contributor.authorCofer, Rufus H.
dc.date.accessioned2017-10-06T16:52:05Z
dc.date.available2017-10-06T16:52:05Z
dc.date.issued1993-11-15
dc.identifier.citationCofer, R. H. (1993). Obscured object detection via bayesian target modeling techniques. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 1942 137-144.en_US
dc.identifier.urihttp://hdl.handle.net/11141/1792
dc.descriptionDecision theory, Probability, Statistical methods, Tracking (position), Underground equipmenten_US
dc.description.abstractUnderground objects are by nature often severely obscured although the general character of the intervening random media may be reasonably understood. The task of detecting these underground objects also implies that their exact location and or orientation is not known. To partially counter these difficulties, one may; however, be given a model of the target of interest, e.g. a particular tank type, a water pipe, etc. To set up a quality framework for solution of the above problem, this paper utilizes the paradigm of Bayesian decision theory that promises minimum error detection given that certain probability density functions can be found. Within this framework, mathematical techniques are shown to handle the uncertainties of target location and orientation, many of the random obscuration problems, and how to make best use of the target model. The approach taken can also be applied to other synergistic cases such as seeing through obscuring vegetation.en_US
dc.language.isoen_USen_US
dc.rightsThis published article is made available in accordance with publishers policy. It may be subject to U.S. copyright law.en_US
dc.rights.urihttp://spie.org/publications/journals/guidelines-for-authors#Terms_of_Useen_US
dc.titleObscured object detection via Bayesian target modeling techniquesen_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.160335


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