Çataltepe, Z.Çetin, A. EnisPearson, T.2016-02-082016-02-0820041522-4880http://hdl.handle.net/11693/27406Date of Conference: 24-27 October 2004Conference Name: International Conference on Image Processing, IEEE 2004We 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.EnglishImage transmittanceKernelsPixel intensityRadial function networksAlgorithmsComputer simulationMarkov processesMathematical modelsMobile telecommunication systemsRobustness (control systems)SensorsImage processingConference Paper10.1109/ICIP.2004.1421723