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http://dx.doi.org/10.6109/jkiice.2017.21.11.2121

Automatic Defect Detection using Fuzzy Binarization and Brightness Contrast Stretching from Ceramic Images for Non-Destructive Testing  

Kim, Kwang Baek (Division of Computer Software Engineering, Silla University)
Song, Doo Heon (Department of Computer Games, Yong-in SongDam College)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.21, no.11, 2017 , pp. 2121-2127 More about this Journal
Abstract
In this paper, we propose a computer vision based automatic defect detection method from ceramic image for non-destructive testing. From region of interest of the image, we apply brightness enhancing stretching algorithm first. One of the strength of our method is that it is designed to detect defects of images obtained from various thicknesses, that is, 8, 10, 11, 16, and 22 mm. In other cases we apply histogram based binarization algorithm. However, for 8 mm case, it may have false positive cases due to weak brightness contrast between defect and noise. Thus, we apply modified fuzzy binarization algorithm for 8 mm case. From the experiment, we verify that the proposed method shows stronger result than our previous study that used Blob labelling for all five thickness cases as expected.
Keywords
Automatic Defect Detection; Ceramic Image; Minimum Filter; Histogram Binarization; Fuzzy Binarization;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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