An efficient dilation-based clustering algorithm for automatic optical inspection

This paper develops an efficient dilation-based clustering algorithm (DBCA) by 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. DBCA is further applied in the post-processing of anti-reflection (AR) glass defect detection in order to justify its practicability. Finally, the experimental results 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.