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A Method of Edge Line Estimation for Panel Glass Images using DBSCAN Algorithm

This paper proposes a method of the edge line estimation using DBSCAN algorithm for panel glass images with heavy distortions. When a panel glass image is taken, edge distortions are generated due to step of the panel glass edge, edge contamination and foreign matter invasion. In order to exclude such distortions, after edge detection, a histogram is obtained using the detected edge position and the edge position variation as data set, and low frequency data is removed. After this process, the edge line was estimated by clustering through the DBSCAN algorithm and applying line-fitting to the cluster of the maximum size. For evaluating performance of the proposed algorithm, various images appearing in actual facilities were used, and the performance was evaluated by comparing it with the RANSAC method, a technique that optimizes by excluding outliers that are commonly used.