Fast insect damage detection in wheat kernels using transmittance images

Author

Cataltepe, Zehra ; Pearson, Tom ; Cetin, Enis

Author_Institution

Intelligent Vision & Reasoning Dept., Siemens Corp. Res. Inc., Princeton, NJ, USA

Volume

2

fYear

2004

fDate

25-29 July 2004

Firstpage

1343

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 0.12 at the true positive rate (sensitivity) of 0.8 and an area under the ROC curve (AUC) of 0.90±0.02. Combining the linear model and a radial basis function network in a committee resulted in a FP rate of 0.09 at the TP Rate of 0.8 and an AUC of 0.93±0.03.

Keywords

feature extraction; image classification; image segmentation; learning (artificial intelligence); radial basis function networks; ROC curve; false positive rate; fast insect damage detection; feature extraction; image classification; image segmentation; learning algorithms; linear model; pixel histogram; radial basis function network; transmittance images; true positive rate; wheat kernel images; Acoustic signal detection; Educational institutions; Electronic mail; Histograms; Infrared detectors; Insects; Kernel; Pixel; Radial basis function networks; Sensitivity;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-8359-1

Type

conf

DOI

10.1109/IJCNN.2004.1380142

Filename

1380142