A new DCT-based image segmentation method for automatic defect detection on an object embedded in noisy low-contrast unbalanced background
2009
Hochschulschrift
Zugriff:
97
This article presents an innovative image segmentation method to extract an object underlying defect detection from its background image. 2-D automatic optical inspection (AOI) technology for defect detection and classification has played a vital role for in-situ manufacturing industrial sectors nowadays. Image segmentation is a crucial step to extract component information from its neighboring background. Due to potential complexity in such an image processing operation, considerable challenges are crucially encountered in establishing a robust approach. In general, three major factors play a significant influence on the result of the segmented image objects: (1) brightness distribution of the background image; (2) degree of unbalanced brightness of the background image; (3) noise level near the object feature to be detected. The research addresses these important factors and develops an effective segmentation method. Exclusive advantage of the method is to overcome the current limitations of the existing SVD (singular value decomposition) or DCT (discrete Cosine transfer) methods. The segmentation performance of the developed method is up to 14% better than the DCT method, in terms of accuracy of segmentation. From the test results on some real industrial cases, it is verified that the method is capable of extracting the tested object desirably.
Titel: |
A new DCT-based image segmentation method for automatic defect detection on an object embedded in noisy low-contrast unbalanced background
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Autor/in / Beteiligte Person: | Chien, Chih-Hung ; 簡志宏 |
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Veröffentlichung: | 2009 |
Medientyp: | Hochschulschrift |
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