Identification of insect damaged wheat kernels using transmittance images

We 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.

• Type: Conference
• Year: 2004
• Where: ICIP
• Authors: A. Enis Çetin, Tom Pearson, Zehra Cataltepe
• Keywords: False Positive Rate, FP Rate, ICIP 2004, Image Processing, TP Rate, True Positive Rate, Un-damaged Wheat Kernels