A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Feasibility of impact-acoustic emissions for detection of damaged wheat kernels
Digital Signal Processing
Discrete-time speech signal processing: principles and practice
Discrete-time speech signal processing: principles and practice
Hardware-based image processing for high-speed inspection of grains
Computers and Electronics in Agriculture
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Mel-and Mellin-cepstral Feature Extraction Algorithms for Face Recognition
The Computer Journal
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Multiple view image analysis of freefalling U.S. wheat grains for damage assessment
Computers and Electronics in Agriculture
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Pattern Recognition Letters
System for removing shell pieces from hazelnut kernels using impact vibration analysis
Computers and Electronics in Agriculture
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Covariance-matrix-based features were applied to the detection of popcorn infected by a fungus that causes a symptom called ''blue-eye''. This infection of popcorn kernels causes economic losses due to the kernels' poor appearance and the frequently disagreeable flavor of the popped kernels. Images of kernels were obtained to distinguish damaged from undamaged kernels using image-processing techniques. Features for distinguishing blue-eye-damaged from undamaged popcorn kernel images were extracted from covariance matrices computed using various image pixel properties. The covariance matrices were formed using different property vectors that consisted of the image coordinate values, their intensity values and the first and second derivatives of the vertical and horizontal directions of different color channels. Support Vector Machines (SVM) were used for classification purposes. An overall recognition rate of 96.5% was achieved using these covariance based features. Relatively low false positive values of 2.4% were obtained which is important to reduce economic loss due to healthy kernels being discarded as fungal damaged. The image processing method is not computationally expensive so that it could be implemented in real-time sorting systems to separate damaged popcorn or other grains that have textural differences.