An Improved Gravity-Orientated Clustering Algorithm and its Application on Anti-Reflection Glass Defect Detection

• Main Authors: Fen-Ning Tien, 田芬寧
• Other Authors: Ming-jong Tsai
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/44462475830798103550

碩士 === 國立臺灣科技大學 === 自動化及控制研究所 === 97 === This thesis proposes Gravity- Orientated Density-Based Clustering Applications with Noise（GDBSCAN）. GDBSCAN separates the query region into four quadrants and calculates each quadrant density individually. The quadrant with the highest density is called Gravity-quadrant. Gravity-quadrant will orientate the expending direction of the density-based clustering algorithm. The proposed method and image process technology are adopted for AR glass defects detection. The detected items include impurities, bright dots and scratch. During the inspection process, a Laplacian operator is employed to enhance the image, firstly. Then, a smoothing process is also taken to reduce the noise before binary process. Subsequently, an adaptive threshold technique is applied to extract the defect images from background of AR glass image. Finally, the AR glass defects is clustered by the proposed GDBSCAN. According to the experimental results, the GDBSCAN can successfully discover the defects of a AR glass image of 300*200 pixels. And it totally takes 21 ms for defects detecting process of each AR glass. The GDBSCAN can save 17% execution time as compared to FDBSCAN and 10% compared to RDBSCAN for an image with higher than 7000 defect points.