Identification of insect damaged wheat kernels using transmittance images

Date
2004
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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
Type
Conference Paper
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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.

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Keywords
Image transmittance, Kernels, Pixel intensity, Radial function networks, Algorithms, Computer simulation, Markov processes, Mathematical models, Mobile telecommunication systems, Robustness (control systems), Sensors, Image processing
Citation
Published Version (Please cite this version)