Automatic defect detection inspectacles and glass bottles based on Fuzzy C Means Clustering

Defects in spectacles and glass bottles result into poor quality and which makes more problems for the manufacturers. Scratches and cracks are typically unavoidable, but their occurrence must be minimized during the production of high-quality glasses. It is a tiresome method to manually inspect very large size glasses. And also the manual inspection process is slow, time-consuming and prone to human error. Automatic defect detection systems using image processing can overcome many of these disadvantages and offer manufacturers an opportunity to significantly improve the product quality and reduce costs. The Potential defects detection process includes image denoising and Fuzzy C Means Clustering Algorithm. Based upon the PSNR value comparison determines the best filtering approach. Then the Universal Image Quality Index shows the segmentation parameters and Pearson correlation coefficient shows the amount of correlation.