Identification of insect damaged wheat kernels using transmittance images
Date
2004
Authors
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Source Title
Proceedings of the International Conference on Image Processing, IEEE 2004
Print ISSN
1522-4880
Electronic ISSN
Publisher
IEEE
Volume
2
Issue
Pages
2917 - 2920
Language
English
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Abstract
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.