Title :
Research of paper surface defects detection system based on blob algorithm
Author :
Xingguang Qi ; Xiaoting Li ; Hailun Zhang
Author_Institution :
Sch. of Electr. Eng. & Autom., Key Lab. of Adv. Manuf. & Meas. & Control Technol. for Light Ind. in Univ. of Shandong, Jinan, China
Abstract :
Effective recognition and localization of paper defect based on machine vision is the key issue for paper defect detection system. This paper proposed an improved algorithm by combination with Blob analysis algorithm and image preprocessing approach to detect the paper defects which exist in captured images by a linear charge coupled device (CCD) camera. First, the defected images are preprocessed, such as image denoising, image segmentation, connectivity analysis, and then extract effective paper textures: defect amount, regional area, long axis, short axis, central position and so on, meanwhile draw the minimum bounding rectangles. Compared with the traditional morphology algorithm and threshold segmentation and fractal feature algorithm, the improved algorithm is validated by a great deal of experimental results with high detection efficiency and defects localization accuracy.
Keywords :
CCD image sensors; computer vision; flaw detection; image denoising; image recognition; image segmentation; image texture; paper; paper industry; production engineering computing; CCD camera; blob algorithm; blob analysis algorithm; captured images; connectivity analysis; defected images; defects localization accuracy; detection efficiency; fractal feature algorithm; image denoising; image preprocessing approach; image segmentation; linear charge coupled device camera; machine vision; minimum bounding rectangles; morphology algorithm; paper defect localization; paper defect recognition; paper surface defects detection system; paper texture extraction; threshold segmentation; Algorithm design and analysis; Educational institutions; Feature extraction; Image denoising; Image segmentation; Instruments; Paper making;
Conference_Titel :
Information and Automation (ICIA), 2013 IEEE International Conference on
Conference_Location :
Yinchuan
DOI :
10.1109/ICInfA.2013.6720384