Shape Feature Extraction and Classification of Food Material Using Computer Vision

Abstract

Food material shape is often closely related to its quality. Due to the demands of high quality, automated food shape inspection has become an important need for the food industry. Currently, accuracy and speed are two major problems for food shape inspection with computer vision. Therefore, in this study, a fast and accurate computer-vision based feature extraction and classification system was developed. In the feature extraction stage, a statistical model-based feature extractor (SMB) and a multi-index active model-based (MAM) feature extractor were developed to improve the accuracy of classifications. In the classification stage, first the back-propagation neural network was applied as a multi-index classifier. Then, to speed up training, some minimum indeterminate zone (MIZ) classifiers were developed. Corn kernels, almonds, and animal-shaped crackers were used to test the above techniques. The results showed that accuracy and speed were greatly improved when the MAM feature extractor was used in conjunction with the MIZ classifier.