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Proceedings Paper

On-line print-defect detecting in an incremental subspace learning framework
Author(s): Xiaogang Sun; Bin Chen; Liang Zhang
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Paper Abstract

Real-time detecting print-defect system is significant for automatically quality control in printing industry. The state-of-the-art detecting algorithms are based on conventional template matching process and usually suffer from false alarm caused by acceptable variations. This paper proposes a novel on-line print-defect detecting approach which uses incremental principal component analysis to model a variety pattern with respect to the detected image itself. The algorithm is constructed and deployed to a real-time detecting print-defect system, and the test results show that the system reduces false alarm dramatically.

Paper Details

Date Published: 19 August 2010
PDF: 6 pages
Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78200C (19 August 2010); doi: 10.1117/12.867450
Show Author Affiliations
Xiaogang Sun, Chengdu Institute of Computer Application (China)
Bin Chen, Chengdu Institute of Computer Application (China)
Liang Zhang, Chengdu Institute of Computer Application (China)


Published in SPIE Proceedings Vol. 7820:
International Conference on Image Processing and Pattern Recognition in Industrial Engineering
Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du, Editor(s)

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