Intelligent segmentation method for real-time defect inspection system

To extract desired flaws from various types of images, the integration of different segmentation methods is required. In this study, we present an intelligent method for automatic selection of a proper image segmentation method upon detecting a particular flaw type. The new method is capable of choosing the most suitable method from four segmentation methods currently available. The automatic selection procedures start from the pre-segmentation of an image to be examined. Then, the predetermined features are extracted from the original, foreground, and background images. After that, a suitable segmentation method will be selected using a classifier based on six features. Finally, the image is re-segmented by the selected segmentation method to discover flaws. The proposed method has been tested using 1676 defective images. The results show a significant reduction in misclassification rate from about 44% to 13.96%.