Abstract
OLED (Organic light-emitting) displays have been called the next generation of display devices for their unique properties: colorful images, large viewing angle, light weight and power efficiency. Complex manufacture processing makes the screen have some defects. Detecting the defects will help to improve the quality. In this paper we concentrate on detecting these defects and proposed a corner-points based method, where the corner-points are extracted from the skeleton image and used as the control points for the subtract operation. We proposed an improved Otsu method to determine the image segmentation threshold by recursive process. Based on the algorithm proposed, a system for OLED screen defect detection was developed. The test result shows that the developed system can detect most of the defects on the panel.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Hyvarinen, A.: Independent component analysis algorithms and applications. Neural Networks (13), 411–430 (2000)
Lin, H.D.: Automated Detection of Color Non-Uniformity Defects in TFT-LCD. Neural Networks (1), 2384–2391 (2006)
Kim, J.H.: A High—speed high—Resolution Vision System for the Inspection of TFT-LCD. In: IEEE International Symposium on Industrial Electronics, vol. (1), pp. 101–105 (2001)
Tachibana, K.: Study on Moire between screen and panel structure in a LCD rear projection for HDTV. In: Display Research Conference, pp. 143–146 (1991)
Lee, K.B.: Defect Detection Method For TFD-LCD Panel Based on Saliency Map Module. In: IEEE Region 10 Conference, vol. A, pp. 223–226 (2004)
Kim, S.H., Kang, T.G., Jeong, D.H.: Region Mura Detection using Efficient High Pass Filtering based on Fast Average Operation. In: The International Federation of Automatic Control, vol. (6), pp. 8190–8195 (2008)
Pratt, W.K., Sawkar, S.S.: Automatic blemish detection in liquid crystal flat panel displays. In: Proc. SPIE, vol. (1), pp. 25–30 (1998)
Tsai, D.M.: Automatic Defect Inspection of Patterned TFT—LCD Panels Using l—D Fourier Reconstruction and Wavelet Decomposition. International Journal of Production Research (3), 4589–4607 (2005)
Lu, C.J.: Defect Defection of Patterned TFT-LCD Surfaces Using Independent Component Analysis. In: Chinese Industrial Engineering Seminar, pp. 1–10 (2004)
Zhang, Y., Zhang, J.: Automatic Blemish Inspection for TFT-LCD Based on Polynomial Surface Fitting. Opto-Electronic Engineering 33(10), 108–114 (2006) (in Chinese)
Zhang, Y.: Application of Fuzzy Expert System in Defect Inspection of TFT-LCD. Journal of Optoelectronics. Laser 17(6), 719–723 (2006)
Lee, J.Y.: Automatic Detection of Region-Mura Defect in TFT-LCD. IEICE Transactions on Information and Systems E87-D(10), 2371–2378 (2004)
Serra, J.: Image Analysis and Mathematical Morphology, vol. (1). Academic Press, San Diego (1982)
Otsu, N.: A threshold selection method from gray-level histogram. IEEE Transactions on System, Man, and Cybernetics SMC-9(1), 62–66 (1979)
Jing, X.J., Cai, A.N., Sun, J.: Image segmentation based on 2D maximum between-cluster variance. Journal of China Institute of Communication 22(4), 71–76 (2001)
Li, L.L., Deng, S.X.: Binarization Algorithm Based on Image Partition Derived from Da-Jing Method. Control & Automation 21(8-3), 76–77 (2005) (in Chinese)
Liu, Y.Y., Gao, J., Zhao, W.M.: Defect Inspection for OLED Display Based on Skeleton Template. Microcomputer & its Applications (2011) (accepted)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gao, J., Wang, Z., Liu, Y., Jian, C., Chen, X. (2012). Development of OLED Panel Defect Detection System through Improved Otsu Algorithm. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33515-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-33515-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33514-3
Online ISBN: 978-3-642-33515-0
eBook Packages: Computer ScienceComputer Science (R0)