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dc.contributor.authorCofer, Rufus H.
dc.contributor.authorKozaitis, S.P.
dc.date.accessioned2017-10-02T17:05:15Z
dc.date.available2017-10-02T17:05:15Z
dc.date.issued2003-08-08
dc.identifier.citationCofer, R. H., & Kozaitis, S. P. (2003). Image chain assessment for feature extraction. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 5108 287-294.en_US
dc.identifier.urihttp://hdl.handle.net/11141/1666
dc.descriptionBayesian detector, Feature extraction, Image chainen_US
dc.description.abstractIt is shown that the image chain has important effects upon the quality of feature extraction. Exact analytic ROC results are given for the case where arbitrary multivariate normal imagery is passed to a Bayesian feature detector designed for multivariate normal imagery with a diagonal covariance matrix. Plots are provided to allow direct visual inspection of many of the more readily apparent effects. Also shown is an analytic tradeoff that says doubling background contrast is equal to halving sensor to scene distance or sensor noise. It is also shown that the results provide a lower bound to the ROC of a Bayesian feature detector designed for arbitrary multivariate normal distributions.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.titleImage chain assessment for feature extractionen_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.487029


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