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dc.contributor.authorYorulmaz, O.en_US
dc.contributor.authorPearson, T. C.en_US
dc.contributor.authorÇetin, A.en_US
dc.date.accessioned2016-02-08T09:46:34Z
dc.date.available2016-02-08T09:46:34Z
dc.date.issued2012en_US
dc.identifier.issn0168-1699
dc.identifier.urihttp://hdl.handle.net/11693/21454
dc.description.abstractCovariance-matrix-based features were applied to the detection of popcorn infected by a fungus that causes a symptom called " blue-eye" . This infection of popcorn kernels causes economic losses due to the kernels' poor appearance and the frequently disagreeable flavor of the popped kernels. Images of kernels were obtained to distinguish damaged from undamaged kernels using image-processing techniques. Features for distinguishing blue-eye-damaged from undamaged popcorn kernel images were extracted from covariance matrices computed using various image pixel properties. The covariance matrices were formed using different property vectors that consisted of the image coordinate values, their intensity values and the first and second derivatives of the vertical and horizontal directions of different color channels. Support Vector Machines (SVM) were used for classification purposes. An overall recognition rate of 96.5% was achieved using these covariance based features. Relatively low false positive values of 2.4% were obtained which is important to reduce economic loss due to healthy kernels being discarded as fungal damaged. The image processing method is not computationally expensive so that it could be implemented in real-time sorting systems to separate damaged popcorn or other grains that have textural differences.en_US
dc.language.isoEnglishen_US
dc.source.titleComputers and Electronics in Agricultureen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.compag.2012.02.012en_US
dc.subjectCorrelation featuresen_US
dc.subjectCovariance featuresen_US
dc.subjectFungus detection on popcorn kernelsen_US
dc.subjectImage processingen_US
dc.subjectSVMen_US
dc.subjectColor channelsen_US
dc.subjectCorrelation featuresen_US
dc.subjectCovariance featuresen_US
dc.subjectCovariance matricesen_US
dc.subjectEconomic lossen_US
dc.subjectFalse positiveen_US
dc.subjectImage coordinatesen_US
dc.subjectImage pixelsen_US
dc.subjectImage processing - methodsen_US
dc.subjectImage propertiesen_US
dc.subjectIntensity valuesen_US
dc.subjectKernel imageen_US
dc.subjectRecognition ratesen_US
dc.subjectSecond derivativesen_US
dc.subjectSorting systemen_US
dc.subjectSupport vector machine (SVM)en_US
dc.subjectSVMen_US
dc.subjectImage processingen_US
dc.subjectLossesen_US
dc.subjectSupport vector machinesen_US
dc.subjectCovariance matrixen_US
dc.subjectComputer simulationen_US
dc.subjectCorrelationen_US
dc.subjectCrop damageen_US
dc.subjectDetection methoden_US
dc.subjectFungal diseaseen_US
dc.subjectFungusen_US
dc.subjectImage analysisen_US
dc.subjectMaizeen_US
dc.subjectMatrixen_US
dc.subjectPixelen_US
dc.subjectVariance analysisen_US
dc.subjectFungien_US
dc.subjectNemophilaen_US
dc.subjectPseudomugilidaeen_US
dc.titleDetection of fungal damaged popcorn using image property covariance featuresen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineering
dc.citation.spage47en_US
dc.citation.epage52en_US
dc.citation.volumeNumber84en_US
dc.identifier.doi10.1016/j.compag.2012.02.012en_US
dc.publisherElsevieren_US


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