Feature Extraction in X-ray Images for Hazelnuts Classification

In the food industry, the importance of automatic detection and selection of raw food ingredients is increasing. In this paper, a method for real time automatic detection, segmentation and classification of hazelnuts using x-ray images is presented. Automatic extraction of independent nut images is made using image processing techniques. To extract meaningful features, moment invariants and texture properties are calculated on global level as well as from co-occurrence matrices. Principal component analysis is applied on features to achieve orthogonality in addition to dimensionality reduction. An anomaly detection algorithm is used for classification. Multivariate Gaussian distributions are calculated for model estimation using training data. Results are calculated on test data by using the threshold value obtained from best validation outcome. The classifier showed 98.6% correct classification rate for negative examples with 0% false negative rate.