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dc.contributor.authorKozaitis, S.P.
dc.contributor.authorCofer, Rufus H.
dc.contributor.authorFoor, Wesley E.
dc.date.accessioned2017-10-06T16:52:45Z
dc.date.available2017-10-06T16:52:45Z
dc.date.issued1996-10-25
dc.identifier.citationKozaitis, S. P., Cofer, R. H., & Foor, W. E. (1993). Design of distortion-invariant correlation filters using supervised learning. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 1959 214-219.en_US
dc.identifier.urihttp://hdl.handle.net/11141/1795
dc.descriptionDesign, Learning systems, Optical correlation, Performance, Statistical methodsen_US
dc.description.abstractWe designed binary phase-only filters from a training set of images using a statistical approach. We forced images into clusters and designed filters to recognize objects from that cluster. We report on results obtained by computer simulation comparing the performance of filters to recognize objects from clusters of one and two classes.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.titleDesign of distortion-invariant correlation filters using supervised learningen_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.160290


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