Identification of insect damaged wheat kernels using transmittance images
Author
Çataltepe, Z.
Çetin, A. Enis
Pearson, T.
Date
2004Source Title
Proceedings of the International Conference on Image Processing, IEEE 2004
Print ISSN
1522-4880
Publisher
IEEE
Volume
2
Pages
2917 - 2920
Language
English
Type
Conference PaperItem Usage Stats
164
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110
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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.
Keywords
Image transmittanceKernels
Pixel intensity
Radial function networks
Algorithms
Computer simulation
Markov processes
Mathematical models
Mobile telecommunication systems
Robustness (control systems)
Sensors
Image processing