Defect classification for LCD color filters using neural-network decision tree classifier

International Journal of Innovative Computing, Information and Control

Volumn 7, Issue 7 A, 2011, Pages 3695-3707

  Tseng, Din Chang ^a   Chung, I Ling ^a,b   Thai, Pei Lin ^a   Chou, Chang Min ^b  

^a NATIONAL CENTRAL UNIVERSITY   (Taiwan)

^b CHING YUN UNIVERSITY   (Taiwan)

Author keywords

Defect classification; Defect description; LCD color filter; Neural network

Indexed keywords

AUTOMATIC DEFECT CLASSIFICATION; COLOR FILTERS; DECISION TREE CLASSIFIERS; DEFECT CLASSIFICATION; DEFECT EXTRACTION; DEFECT TYPE; FEATURE DESCRIPTION; INPUT SIGNAL; NON-LINEAR RELATIONSHIPS; PRODUCTION PROCEDURE; THREE STAGES; TRAINING METHODS;

COLOR; DECISION TREES; NEURAL NETWORKS; PLANT EXTRACTS;

DEFECTS;

EID: 79959911406     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (20)

1. K.-H. Hsia, S.-F. Lien, C.-C. Wang, T.-E. Lee and J.-P. Su, Camera position estimation from image by neural network, ICIC Express Letters, vol.3, no. 4A, pp.1161-1166, 2009.
2. T. Kondo and J. Ueno, Medical image recognition of abdominal multi-organs by RBF GMDH-type neural network, International Journal of Innovative Computing, Information and Control, vol. 5, no. 1, pp.225-240, 2009.
3. A. Kumar, Neural network based detection of local textile defects, Pattern Recognition, vol.36, no.7, pp. 1645-1659, 2003.
4. L.-F. Chen, C.-T. Su and M.-H. Chen, A neural-network approach for defect recognition in TFT-LCD photolithography process, IEEE Trans, on Electronics Packaging Manufacturing, vol. 32, pp.1-8, 2009.
5. K.-L. Hsieh, Incorporating ANNs and statistical techniques into achieving process analysis in TFT-LCD manufacturing industry, Robotics and Computer-Integrated Manufacturing, vol.26, pp.92-99, 2010.
6. Y. E. Shao and B.-S. Hsu, Determining the contributors for a multivariate SPC chart signal using artificial neural networks and support vector machine, International Journal of Innovative Computing, Information and Control, vol. 5, no.12B, pp.4899-4906, 2009.
7. X. Chen, J. Yu, M. Gu and X. Tang, A new GPS/SINS integrated navigation fusion algorithm based on RBF neural network and Kalman filtering, ICIC Express Letters, vol. 3, no. 3B, pp.841-846, 2009.
8. X. Xie, A review of recent advances in surface defect detection using texture analysis techniques, Electronic Letters on Computer Vision and Image Analysis, vol.7, no.3, pp.1-22, 2008.
9. L. Latif-Amet, A. Ertuzun and A. Ercil, An efficient method for texture defect detection: subband domain co-occurrence matrices, Image and Vision Computing, vol.18, nos.6-7, pp.543-553, 2000.
10. C. Palm, Color texture classification by integrative co-occurrence matrices, Pattern Recognition, vol.37, no.5, pp.965-976, 2004.
11. A. Malhi and R. X. Gao, PCA-based feature selection scheme for machine defect classification, IEEE Trans. Instrumentation and Measurement, vol.53, no.6, pp. 1517-1525, 2004.
12. X. H. Xie, M. Mirmehdi and B. Thomas, Color tonality inspection using eigenspace features, Machine Vision and Applications, vol.16, no.6, pp.364-373, 2006.
13. F. Vilnrotter, R. Nevatia and K. Price, Structural analysis of natural textures, IEEE Trans, on Pattern Analysis and Machine Intelligence, vol.8, pp.76-89, 1986.
14. A. Kumar and G. Pang, Defect detection in textured materials using optimized filters, IEEE Trans, on System, Man, and Cybernetics-Part B: Cybernetics, vol.32, no.5, pp.553-570, 2002.
15. D.-M. Tsai and C.-H. Yang, A quantile-quantile plot based pattern matching for defect detection, Pattern Recognition Letters, vol.26, pp.1948-1962, 2005.
16. C.-J. Lu and D.-M. Tsai, Independent component analysis-based defect detection in patterned liquid crystal display surfaces, Image and Vision Computing, vol.26, pp.955-970, 2008.
17. D.-M. Tsai and S.-C. Lai, Defect detection in periodically patterned surfaces using independent component analysis, Pattern Recognition, vol.41, pp.2812-2832, 2008.
18. Y.-H. Tseng and D.-M. Tsai, Defect detection of uneven brightness in low-contrast images using basis image representation, Pattern Recognition, vol.43, pp.1129-1141, 2010.
19. S. Haykin, Neural Network: A Comprehensive Foundation, Prentice Hall Press, 2nd Ed., 1999.
20. N. Otsu, A threshold selection method from gray-level histograms, IEEE Trans, on System Man and Cybernetics, vol.9, no.1, pp.62-66, 1979.