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dc.contributor.authorCataltepe, Z.en_US
dc.contributor.authorCetin, E.en_US
dc.contributor.authorPearson, T.en_US
dc.date.accessioned2016-02-08T11:52:33Z
dc.date.available2016-02-08T11:52:33Z
dc.date.issued2004en_US
dc.identifier.issn15224880
dc.identifier.urihttp://hdl.handle.net/11693/27406
dc.description.abstractWe used transmittance images and different learning algorithms to classify insect damaged and un-damaged wheat kernels. Using the histogram of the pixels of the wheat images as the feature, and the linear model as the learning algorithm, we achieved a False Positive Rate (1-specificity) of 0.2 at the True Positive Rate (sensitivity) of 0.8 and an Area Under the ROC Curve (AUC) of 0.86. Combining the linear model and a Radial Basis Function Network in a committee resulted in a FP Rate of 0.1 at the TP Rate of 0.8 and an AUC of 0.92. © 2004 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.titleProceedings - International Conference on Image Processing, ICIPen_US
dc.subjectImage transmittanceen_US
dc.subjectKernelsen_US
dc.subjectPixel intensityen_US
dc.subjectRadial function networksen_US
dc.subjectAlgorithmsen_US
dc.subjectComputer simulationen_US
dc.subjectMarkov processesen_US
dc.subjectMathematical modelsen_US
dc.subjectMobile telecommunication systemsen_US
dc.subjectRobustness (control systems)en_US
dc.subjectSensorsen_US
dc.subjectImage processingen_US
dc.titleIdentification of insect damaged wheat kernels using transmittance imagesen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineering
dc.citation.spage2917en_US
dc.citation.epage2920en_US
dc.citation.volumeNumber2en_US


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