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- W2179556567 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." @default.
- W2179556567 created "2016-06-24" @default.
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- W2179556567 date "2010-08-07" @default.
- W2179556567 modified "2023-10-18" @default.
- W2179556567 title "On-line print-defect detecting in an incremental subspace learning framework" @default.
- W2179556567 doi "https://doi.org/10.1117/12.867450" @default.
- W2179556567 hasPublicationYear "2010" @default.
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