DocumentCode
3448481
Title
A Real-Time Vision System for Defect Detection in Printed Matter and Its Key Technologies
Author
Ou, Yang ; Baoping, Guo ; Tao, Hu ; Xuan, Guo
Author_Institution
Huazhong Univ. of Sci. & Technol., Wuhan
fYear
2007
fDate
23-25 May 2007
Firstpage
2157
Lastpage
2161
Abstract
A vision inspection system with high speed and on line is proposed to detect defects in printed matter, such as smudges, doctor streaks, pin holes, character misprints, foreign matters, hazing, and wrinkles, etc. Any tiny defect would be developed by using the high and low illumination angles design and anti-blooming techniques. A new image reference method based on morphological pre-processing eliminates all false defects brought by slight distortion of printed matter and chromatography mistake. The fast objects searching algorithm based on run-length-encoding can locate the coordinates of defects and define the shape of the defects. The C/S parallel network structure was used, image data were processed distributed and quality data is managed centralized. Experimental results verify the speed, reliability and accuracy of proposed system.
Keywords
chromatography; computer vision; image morphing; inspection; object detection; printing industry; production engineering computing; anti-blooming techniques; chromatography mistake; defect detection; illumination angles; image reference method; morphological pre-processing; objects searching algorithm; printed matter; real-time vision system; vision inspection system; Charge-coupled image sensors; Fluorescence; Image processing; Inspection; Light sources; Lighting; Machine vision; Optical reflection; Printing; Real time systems; Image processing; defect of detection; gray morphology; machine vision; printed matter;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics and Applications, 2007. ICIEA 2007. 2nd IEEE Conference on
Conference_Location
Harbin
Print_ISBN
978-1-4244-0737-8
Electronic_ISBN
978-1-4244-0737-8
Type
conf
DOI
10.1109/ICIEA.2007.4318792
Filename
4318792
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