Flaw detection on decorated glasses by color image processing

This work presents a method which detects aspect flaws occurring on the color surfaces of drinking glasses decorated thanks to an industrial silk-screen process. As the pattern printed on glasses slightly varies between two glasses successively produced, a simple comparison between a reference image which represents a glass without any flaw and the current image which contains the glass to be inspected, provides poor results for flaw detection. That's why we propose an original color image segmentation scheme in order to compare the segmentation of the reference image and those of the current image to be inspected. This procedure iteratively constructs the pixel classes by histogram multi-thresholding. For this purpose, the most discriminating color spaces are automatically selected during an off-line supervised learning scheme so that the color image segmentation is achieved by pixel classification.