Browse > Article
http://dx.doi.org/10.9717/kmms.2016.19.3.626

Mura Defect Enhancement based on Saliency Map in TFT-LCD Image  

Lee, Eun Young (Electronics Engineering, Kyunpook National University)
Park, Kil Houm (Electronics Engineering, Kyunpook National University)
Publication Information
Abstract
In this paper, we propose the defect emphasis in TFT-LCD panel image. The defect emphasis image consist of S(Shape) map and B(Brightness) map. S map based on DoG(difference of gaussian) is made with the mura defect shape characteristic. And B map use defect intensity property that defect intensity is higher than background. The experiments were conducted to evaluate the performance of the proposed defect emphasis method. The results of experiments show the validity of the defect emphasis using the proposed method.
Keywords
TFT-LCD; Defect Enhancement; Mura Defect;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Y. Ishii, "The World of Liquid-Crystal Display TVs-Past, Present and Future," Journal of Display Technology, Vol. 3, No. 4, pp. 351-360, 2007   DOI
2 L.Y. Pan, S.C. Chang, M.Y. Liao, and Y.T. Lin, "The Future Development of Global LCD TV Industry," Management of Engineering and Technology, Portland International Center for. IEEE, 2007.
3 C.H. Noh, S.L. Lee, and M.S. Zo, “An effective classification method for TFT-LCD film detect images using intensity distribution and shape analysis,” Journal of Korea Multimedia Society, Vol. 13, No. 8, pp. 1115-1127, 2010.
4 E.Y. Lee and K.H. Park, “TFT-LCD Defect Blob Detection Based on Sequential Defect Detection Method,” Journal of the Korea Industrial Information Systems Research, Vol. 20, No. 2, pp. 73-83, 2015.   DOI
5 J.H. Oh and K.H. Park, “TFT-LCD Defect Enhancement Using Frequency Sensitivity of HVS,” Journal of The Institute of Electronics Engineers, Vol. 44-SP, No. 5, pp. 20-27, 2007.
6 H. Wang, S.Z. Li, and Y. Wang, “The Quotient Image: Class-based Re-Rendering and Ecognition with Varying Illuminations,“ IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 2, pp. 129- 139, 2001.   DOI
7 H. Wang, S.Z. Li, and Y. Wang, "Generalized Quotient Image," Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 12, pp. 498-505, 2004.
8 S.I. Baek, W.S. Kim, T.M. Koo, I. Choi, and K.H. Park "Inspection of Defect on LCD Panel Using Ploynomial Approximation," Proceeding of TENCON, Vol. 1, pp. 235-238, 2004.
9 W.I. Park, K.B. Lee, S.Y. Kim, and K.H. Park, “TFT-LCD Defect Detection Using Double- Self Quotient Image,” Journal of KIISE: Computing Practices and Letter, Vol. 14, No. 6, pp. 604-608, 2008.
10 Y. Zhang and J. Zhang, "A Fuzzy Neural Network Approach for Quantitative Evaluation of Mura in TFT-LCD," Proceedings of International Conference on Neural Networks and Brain, pp. 424-427, 2005.
11 J.S. Ryu, J.H. Oh, J.G. Kim, T.M. Koo, and K.H. Park, "TFT-LCD Panel Blob-Mura Inspection Using the Correlation of Wavelet Coefficients," Proceedings of TENCON, Vol. 1, pp. 219-222, 2004.
12 P. Birch, B. Mitra, N.M. Bangalore, S. Rehman, R. Young, and C. Ctatwin, “Approximate Bandpass and Frequency Response Models of the Difference of Gaussian Filter,” Journal ofOptics Communications, Vol. 283, No. 24, pp. 4942-4948, 2010.   DOI