NDLI: Low-contrast surface inspection of mura defects in liquid crystal displays using optical flow-based motion analysis

Content Provider	Springer Nature Link
Author	Tsai, Du Ming Tsai, Hsin Yang
Copyright Year	2010
Abstract	This paper proposes a machine vision scheme for mura defect detection in LCD manufacturing. Mura is a Japanese word for blemish, which typically shows brightness imperfections from its surroundings in the surface. It appears as a low-contrast region without clear edges. Traditional automatic visual inspection algorithms detect mura defects from individual still images. They neglect that a mura defect may not be visually sensed in the image from a stationary system. In this study, the LCD panel is assumed to move along a track. While the panel passes through a fixed camera, the light reflection from different angles can effectively enhance the mura defect in the low-contrast images. The mura detection problem is therefore treated as a motion analysis in image sequences using optical flow techniques. Since a LCD panel moves along a single direction, both two-dimensional and one-dimensional optical flow methods are developed. Three discriminative features based on flow magnitude, mean flow magnitude and flow density in the optical flow field are presented to extract the defective regions. Both real panel images and synthetic surface images are used to evaluate the efficacy of the proposed methods. Experimental results have shown that the proposed 1D optical flow method works as well as the 2D optical flow method to detect very low-contrast mura defects of small size, and achieves a high processing rate around 20 frames per second for images of size 200 × 200.
Starting Page	629
Ending Page	649
Page Count	21
File Format	PDF
ISSN	09328092
Journal	Machine Vision and Applications
Volume Number	22
Issue Number	4
e-ISSN	14321769
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-03-31
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Surface inspection Defect detection Motion images Optical flow Mura Liquid crystal display Communications Engineering, Networks Pattern Recognition Image Processing and Computer Vision
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Computer Science Applications Software Hardware and Architecture

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