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dc.contributor.authorHazlett, Thomas L.
dc.contributor.authorCofer, Rufus H.
dc.contributor.authorBrown, Harold K.
dc.date.accessioned2017-10-06T18:19:43Z
dc.date.available2017-10-06T18:19:43Z
dc.date.issued1992-09-16
dc.identifier.citationHazlett, T. L., Cofer, R. H., & Brown, H. K. (1992). Explanation mode for bayesian automatic object recognition. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 1700 258-268.en_US
dc.identifier.urihttp://hdl.handle.net/11141/1846
dc.descriptionAutomation, Decision theory, Expert systems, Probability, Statistical methodsen_US
dc.description.abstractOne of the more useful techniques to emerge from AI is the provision of an explanation modality used by the researcher to understand and subsequently tune the reasoning of an expert system. Such a capability, missing in the arena of statistical object recognition, is not that difficult to provide. Long standing results show that the paradigm of Bayesian object recognition is truly optimal in a minimum probability of error sense. To a large degree, the Bayesian paradigm achieves optimality through adroit fusion of a wide range of lower informational data sources to give a higher quality decision - a very 'expert system' like capability. When various sources of incoming data are represented by C++ classes, it becomes possible to automatically backtrack the Bayesian data fusion process, assigning relative weights to the more significant datums and their combinations. A C++ object oriented engine is then able to synthesize 'English' like textural description of the Bayesian reasoning suitable for generalized presentation. Key concepts and examples are provided based on an actual object recognition problem.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.titleExplanation mode for Bayesian automatic object recognitionen_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.138270


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