A Novel Automated Inspection Approach Based on Adaptive Region-Growing Image Segmentation

摘要

The region-growing algorithm is commonly used for image segmentation because the algorithm can identify regions by selecting seed points. This study presents a novel algorithm for adaptive region growing based on neural networks, which is highly effective as a region-growing technique for automated inspection. The algorithm transforms input images into a gray-level space and then adaptively segments the images by merging regions based on artificial neural networks, which classify the image patterns according to shape descriptors of moment-based invariants. This approach can automatically produce segmented images with optimal shape descriptors for inspection. The proposed method performs well in automated inspection tests and produces superior results to existing methods of image segmentation.