Article

DOI: 10.5937/jaes16-16888
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions.

Volume 16 article 537 pages: 333 - 342

Yuan-Shyi Peter Chiu
Department of Industrial Engineering and Management,
Chaoyang University of Technology,Wufong District, Taiwan

Hong-Dar Lin
Department of Industrial Engineering and Management,
Chaoyang University of Technology,Wufong District, Taiwan

Touch screens (TSs) are commonly applied in many electronic appliances such as smartphones, tablets, etc. Currently, capacitive touch screens (CTSs) are the main touch technology of screen panels due to many excellent electronic properties. Problems exist in inspecting flaws inlaid in appearances of CTSs with structural patterns. Area flaws are a type of common visual defect that comprises dust, bubbles, ripple marks, and other flaws of bigger sizes. These flaws have the attributes of low contrast, brightness with slow changes, unusual and non-orientation forms, and sometimes both bright and dark flaws existing at the same time in a region. This paper suggests image models based on transformation filtering to inspect the area flaws on appearances of CTSs. We apply the Haar wavelet transform with flat zone filtering technique to eliminate the structural patterns of background by means of filtering an approximate sub-image of a breakdown wavelet domain image. Subsequently, the filtered image is reversely transformed to obtain a rebuilt image in spatial domain. Last, the rebuilt image with intensified flaws can be simply partitioned into three species (black flaws, gray flaws, and white background) by using a statistical interval estimation method. Therefore, the intricate area flaws are precisely identified by the suggested scheme. We contrast our approach with three traditional methods with real samples under complex background and conduct quantitative comparisons. The effectiveness and accuracy of the developed image models are confirmed by expert assessments, as well as by comparative analysis with the known methods in the field of spatial localizations and production-related effects of flaw detection.

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Authors deeply thank the Ministry of Science and Technology of Taiwan for sponsor of this study (under grant no. MOST103-2221-E-324 -036).

Lin, Y.K., Huang, C.F., Chang, P.C. (2013). System reliability evaluation of a touch panel manufacturing system with defect rate and reworking. Reliability Engineering and System Safety, 118 (10), 51-60.

Hung, M.H., Hsieh, C.H. (2015). A novel algorithm for defect inspection of touch panels. Image and Vision Computing, 41, 11-25.

Liang, L.Q., Li, D., Fu, X., Zhang, W.J. (2016). Touch screen defect inspection based on sparse representation in low resolution images.Multimedia Tools and Applications, 75(5), 2655-2666.

Lin, H.D., Li, J.M. (2014). Automated area defect inspection of touch panels using computer vision. 2014 Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV 2014), p. 16-22.

Jiang, C.C., Quan, Y.M., Lin, X.U. (2016). Defect detection of capacitive touch panel using a nonnegative matrix factorization and tolerance model. Applied Optics, 55(9), 2331-2338.

Murphy, J.N., Harris, K.D.,Buriak, J.M. (2015). Automated defect and correlation length analysis of block copolymer thin film nanopatterns.PLoS ONE, 10(7): e0133088.

Liu, J., Tang, Z., Zhang, J., Chen, Q., Xu, P., Liu, W. (2016). Visual perception-based statistical modeling of complex grain image for product quality monitoring and supervision on assembly production line.PLoS ONE, 11(3): e0146484.

Huang, S.H., Pan, Y.C. (2015). Automated visual inspection in the semiconductor industry: A survey. Computers in Industry, 66, 1-10.

Lin, H.D.,Chiu, Y.P. (2010). RBF network and EPC method applied to automated process regulations for passive components dicing, International Journal of Innovative Computing Information and Control, 6(11), 5077-5091.

Adamo, F., Attivissimo, F., Nisio, A.Di., Savino, M. (2009). A low-cost inspection system for online defects assessment in satin glass. Measurement, 42, 1304-1311.

Liu,H., Wang, Y., Duan, F. (2008).Glass bottle inspector based on machine vision. International Journal of Computer Systems Science and Engineering, 3(3), 162-167.

Sezgin, M., Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13(1), 146-156.

Otsu, N. (1979). A threshold selection method from gray level histogram. IEEE Transactions on Systems, Man and Cybernetics, 9, 62-66.

Ng, H. F. (2006). Automatic thresholding for defect detection. Pattern Recognition Letters, 27, 1644-1649.

Navarro, P., Iborra, A., Fernández, C., Sánchez, P., Suardíaz, J. (2010). A sensor system for detection of hull surface defects. Sensors, 10, 7067-7081.

Gonzalez, R.C., Woods, R.E. (2008). Digital Image Processing. 3rd Ed., Prentice Hall, New Jersey, USA.

Nasira, G.M., Banumathi, P. (2013). Fourier transform and image processing in automated fabric defect inspection system. International Journal of Computational Intelligence and Informatics, 3(1), 61-64.

Tsai, D.M., Hsiao, B. (2001). Automatic surface inspection using wavelet reconstruction. Pattern Recognition, 34, 1285-1305.

Lin, H.D. (2007). Automated visual inspection of ripple defects using wavelet characteristic based multivariate statistical approach. Image and Vision Computing, 25, 1785-1801.

Li, T.S. (2009). Applying wavelets transform and support vector machine for copper clad laminate defects classification. Computers & Industrial Engineering, 56, 1154-1168.

Chang, H.F. (2012). Design and implementation of real-time fabric defect detection system. Advances in information Sciences and Service Sciences, 4(21), 23-30.

Lin, H.D., Chiu, S.W. (2011). Flaw detection of domed surfaces in LED packages by machine vision system. Expert Systems with Applications, 38, 15208-15216.

Lu, C.J., Tsai, D.M. (2008). Independent component analysis-based defect detection in patterned liquid crystal display surfaces. Image and Vision Computing, 26, 955-970.

Chen, Y.C., Yu, J.H.,Xie, M.C.,Shiou, F.J. (2011).Automated optical inspection system for analogical resistance type touch panel. International Journal of the Physical Sciences, 6(22), 5141-5152.

Lin, H.D., Tsai, H.H. (2012). Automated quality inspection of surface defects on touch panels. Journal of the Chinese Institute of Industrial Engineers, 29(5), 291-302.

Mallat, S.G. (1989). A theroyfor mulitiresolutionsignal decomposition the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693.

Shirazi, M.N., Noda, H., Takao, N. (2000). Texture classification based on Markov modeling in wavelet feature space. Image and Vision Computing, 18, 967-973.

Arivazhagan, S.,Ganesan, L. (2003). Texture segmentation using wavelet transform. Pattern recognition Letters, 24, 3197-3203.

Tsai, D.M., Chiang, C.H. (2003). Automated band selection for wavelet reconstruction in the application of defect detection. Image and Vision Computing, 21, 413-431.

Montgomery, D.C. (2009).Statistical Quality Control: A Modern Introduction,6th Edition, John Wiley & Sons, New York, NY, USA.

Nixon, M.S., Aguado, A.S. (2008). Feature Extraction and Image Processing.2nd Edition, Academic Press, Elsevier, Oxford, UK.

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