Cited By
View all- Ming WZhang SLiu XLiu KYuan JXie ZSun PGuo X(2021)Survey of Mura Defect Detection in Liquid Crystal Displays Based on Machine VisionCrystals10.3390/cryst1112144411:12(1444)Online publication date: 24-Nov-2021
Effective defect classification for flat display panel film images
Pages 264 - 267
Abstract
In this paper, we propose an effective defect classification system for FDP (flat display panel) film images which are acquired in real production lines. A film image is segmented into a binary image with two non-overlapping regions: defect and non-defect regions. From the defect regions, various features are extracted such as brightness distribution, linearity, and morphologic characteristics. The film defects are classified through the analysis of those features extracted. Empirical study shows our system classifies five types of film defects effectively.
References
[1]
Baek S. I., Kim W. S., Koo T. M., Choi I.&Park K. H., Inspection of defect on LCD panel using polynomial approximation. In: Proceedings of IEEE Region 10 Conference, Vol. 1, 2004, 235--238.
[2]
Jiang B. C., Wang C. C.&Liu H. C., Liquid crystal display surface uniformity defect inspection using analysis of variance and exponentially weighted moving average techniques. International Journal of Production Research, 43(1), 2005, 67--80.
[3]
Kim W. S., Kwak D. M., Song Y. C., Choi D. H.&Park K. H., Detection of Spot-Type Defects on Liquid Crystal Display Modules. Key Engineering Materials, Vol. 270--273, 2004, 808--813.
[4]
Lee J. Y.&Yoo S. I., Automatic Detection of Region-Mura Defect in TFT-LCD. IEICE TRANS. INF.&SYSE, E87-D(10), 2004, 2371--2378.
[5]
Lu C. J. and Tsai D. M., "Defect inspection of patterned thin film transistor-liquid crystal display panels using a fast sub-image-based singular value decomposition." International Journal of Production Research, Vol. 42, No. 20, 2004, pp. 4331--4351.
[6]
Mori G., Belongie S.&Malik J., Efficient shape matching using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11), 2005, 1832--1837.
[7]
Oh J. H., Kwak D. M., Lee K. B., Song Y. C., Choi D. H.&Park K. H., Line Defect Detection in TFT-LCD Using Directional Filter Bank and Adaptive Multilevel Thresholding. Key Engineering Materials, Vol. 270--273, 2004, 233--238.
[8]
Yoon Y. G., Lee S. L., Chung C. W.&Kim S. H., An effective defect inspection system for polarized film images using image segmentation and template matching techniques. Computers&IE, 55(3), 2008, 567--583.
Index Terms
- Effective defect classification for flat display panel film images
Recommendations
Imbalanced defect classification for mobile phone screen glass using multifractal features and a new sampling method
Defect classification has drawn significant attention in the mobile phone screen glass (MPSG) manufacturing field because it helps to determine problems in the manufacturing process. Two problems exist in MPSG defect classification: (1) the high ...
Defect Classification of Electronic Board Using Dense SIFT and CNN
AbstractThis paper proposes a new defect classification method of electronic board using Dense SIFT and CNN which can represent the effective features to the gray scale image. Proposed method does not use any reference image and effective keypoints are ...
Effective automatic defect classification process based on CNN with stacking ensemble model for TFT-LCD panel
AbstractThe classification of defect types during LCD panel production is very important because it is closely related to deciding whether a defect panel is restorable. But since defect areas are very small compared to the panel area, it is hard to ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
August 2009
687 pages
ISBN:9781605586625
DOI:10.1145/1644993
- General Chairs:
- Geuk Lee,
- Daniel Howard,
- Jeong Jin Kang,
- Program Chairs:
- Dominik Slezak,
- Tae Nam Ahn,
- Chung-Huang Yang
Copyright © 2009 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 August 2009
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
ICHIT '09
ICHIT '09: International Conference on Convergence and Hybrid Information Technology
August 27 - 29, 2009
Daejeon, Korea
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 31Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 22 Dec 2024
Other Metrics
Citations
Cited By
View all- Ming WZhang SLiu XLiu KYuan JXie ZSun PGuo X(2021)Survey of Mura Defect Detection in Liquid Crystal Displays Based on Machine VisionCrystals10.3390/cryst1112144411:12(1444)Online publication date: 24-Nov-2021
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in