A computer vision system for wineglass defect inspection via Gabor-filter-based texture features
Computer vision inspection systems are widely used for on-line inspection and quality control to improve the finished product quality and lower the costs. In this study, a novel system for wineglass defect inspection has been presented. A carrier grating is used to carry spatial information about inspected glasses. To demodulate the regular/irregular information, an analysis approach for extracting texture features based on Gabor filter has been proposed. From global response surfaces or feature images, objects of interest are segmented. Next local response surfaces or feature images over the isolated regions are calculated. Lastly texture features describing their smoothness and uniformity are computed based on a gray-level co-occurrence matrix and extracted from the local feature images. To make an acceptance/rejection decision, these features are fed to a conventional back propagation classifier. The proposed inspection system has been implemented and tested with some wineglass samples.
computer vision wineglass defect inspection gabor-filter-based texture features neural network classifier