A Dilation-based Clustering Algorithm for Anti-Reflection Glass Inspection

This paper develops an efficient and effective dilation-based clustering algorithm (DBCA) for Anti-Reflection (AR) glass defect detection using run-length encoding (RLE). The fundamental concept of dilation-based connectivity and its limitation are described in the beginning. Subsequently, the architecture of DBCA is constructed in the following procedures: (1) run-length encoding, (2) RLE-based morphological operation, (3) RLE-based component detection algorithm, (4) relationship construction, and (5) re-labeling connection. The details of these five procedures performed in DBCA are then discussed in detail. Finally, the experimental results of DBCA indicate that this algorithm can successfully overcome the effects of broken defects for AR glass if an appropriate structure element is selected. Moreover, the performance evaluation further shows that DBCA can be applied in the real application as a post-processing of defect inspection.