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dc.contributor.authorYüceer, C.en_US
dc.contributor.authorOflazer, K.en_US
dc.date.accessioned2015-07-28T11:56:07Z
dc.date.available2015-07-28T11:56:07Z
dc.date.issued1993en_US
dc.identifier.issn0031-3203
dc.identifier.urihttp://hdl.handle.net/11693/10857en_US
dc.description.abstractThis paper describes a hybrid pattern classification system based on a pattern preprocessor and an artificial neural network classifier that can recognize patterns even when they are deformed by transformation of rotation, scaling, and translation or a combination of these. After a description of the system architecture we provide experimental results from three different classification domains: classification of letters in the English alphabet, classification of the letters in the Japanese Katakana alphabet, and classification of geometric figures. For the first problem, our system can recognize patterns deformed by a single transformation with well over 90% success ratio and with 89% success ratio when all three transformations are applied. For the second problem, the system performs very good for patterns deformed by scaling and translation but worse (about 75%) when rotations are involved. For the third problem, the success ratio is almost 100% when only a single transformation is applied and 88% when all three transformations are applied. The system is general purpose and has a reasonable noise tolerance. Times Cited: 32 (from All Databen_US
dc.language.isoEnglishen_US
dc.source.titlePattern Recognitionen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/0031-3203(93)90122-Den_US
dc.subjectDeformation Invariant Pattern Classificationen_US
dc.subjectPattern Recognitionen_US
dc.subjectArtificial Neural Networksen_US
dc.titleA rotation, scaling, and translation invariant pattern classification systemen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage687en_US
dc.citation.epage710en_US
dc.citation.volumeNumber26en_US
dc.citation.issueNumber5en_US
dc.identifier.doi10.1016/0031-3203(93)90122-Den_US
dc.publisherElsevieren_US
dc.identifier.eissn1873-5142


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