On-line print-defect detecting in an incremental subspace learning framework

Xiaogang Sun; Bin Chen; Liang Zhang

doi:10.1117/12.867450

19 August 2010 On-line print-defect detecting in an incremental subspace learning framework

Xiaogang Sun, Bin Chen, Liang Zhang

Proceedings Volume 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering; 78200C (2010) https://doi.org/10.1117/12.867450
Event: International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 2010, Xi'an, China

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.

Citation Download Citation

Xiaogang Sun, Bin Chen, and Liang Zhang "On-line print-defect detecting in an incremental subspace learning framework", Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78200C (19 August 2010); https://doi.org/10.1117/12.867450

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KEYWORDS

Principal component analysis

Printing

Defect detection

Distortion

Control systems

Image processing

Image restoration

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