SHAPE FEATURE-EXTRACTION AND CLASSIFICATION OF FOOD MATERIAL USING COMPUTER VISION

Food material shape is often closely related to its qualify. Due to th e 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 fea ture extraction stage, a statistical model based feature extractor (SM B) and a multi-index active model-based (MAM) feature extractor were d eveloped to improve the accuracy of classifications. In the classifica tion stage, first the back-propagation neural network was applied as a multi-index classifier. Then, to speed up training, some minimum inde terminate zone (MIZ) classifiers were developed. Corn kernels, almonds , and animal-shaped crackers were used to rest the above techniques. T he results showed that accuracy and speed were greatly improved when t he MAM feature extractor was used in conjunction with the MIZ classifi er.