How to cite this paper
Lin, H., Lee, T., Lin, C & Chiu, Y. (2022). Exploring quality inspection and grade judgment of distortion defects in the fabrication of spectacle lenses.Decision Science Letters , 11(4), 497-508.
Refrences
Abu Ebayyeh, A.M., & Mousavi, A. (2020). A review and analysis of automatic optical inspection and quality monitoring methods in electronic industry. IEEE Access, 8, 183192-183271.
Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., & Shao, L. (2021). Surface defect detection methods for industrial products: a review. Applied Sciences, 11(16), 7657.
Chiu, Y.-S. P., & Lin, H.-D. (2013). An innovative blemish detection system for curved LED lenses. Expert Systems with Applications, 40(2), 471-479.
Cutolo, F., Fontana, U., Cattari, N., & Ferrari, V. (2020). Off-line camera-based calibration for optical see-through head-mounted displays. Applied Sciences, 10(193), 1-19.
Daniel, S.C., Luis, G., Miguel, A.F., Agustin, S., Julio, E., Luis, M., & Luis, A. (2017). Automatic correction of perspective and optical distortions. Computer Vision and Image Understanding, 161, 1-10.
Dixon, M., Glaubius, R., Freeman, P., Pless, R., Gleason, M.P., Thomas, M.M., & Smart, W. (2011). Measuring optical distortion in aircraft transparencies: a fully automated system for quantitative evaluation. Machine Vision and Applications, 22(5), 791-804.
Gerton, K.M., Novar, B.J., Brockmeier, W., & Putnam, C. (2019). A novel method for optical distortion quantification. Optometry and Vision Science, 96(2), 117-123.
Hou, Y., Zhang, H., Zhao, J., Jian, H., Hao, Q., Liu, Z., & Guo, B. (2018). Camera lens distortion evaluation and correction technique based on a colour CCD moiré method. Optics and Lasers in Engineering, 110, 211-219.
Jang, J.S.R. (1993). ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.
Karangwa, J., Kong, L.H., Yi, D.R., & Zheng, J.S. (2021). Automatic optical inspection platform for real-time surface defects detection on plane optical components based on semantic segmentation. Applied Optics, 60(19), 5496-5506.
Karaboga, D., & Kaya, E. (2018). Adaptive network-based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artificial Intelligence Review, 52(4), 2263-2293.
Kuo, C.-F. J., Lo, W.-C., Huang, Y.-R., Tsai, H.-Y., Lee, C.-L., & Wu, H.-C. (2017). Automated defect inspection system for CMOS image sensor with micro multi-layer non-spherical lens module. Journal of Manufacturing Systems, 45, 248-259.
Le, N.T., Wang, J.-W., Wang, C.-C., & Nguyen, T.N. (2019). Automatic defect inspection for coated eyeglass based on symmetrized energy analysis of color channels. Symmetry, 11, 1518.
Li, K., & Su, H. (2010). Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system. Energy and Buildings, 42, 2070-2076.
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.
Lin, H.-D., & Lo, Y.-C. (2016). Automated distortion defect inspection of transparent glass using computer vision. Proceedings of the 2016 International Conference on Image Processing, Computer Vision, and Pattern Recognition, 235-240.
Lin, H.-D., & Hsieh, K.-S. (2018). Detection of surface variations on curved mirrors of vehicles using slight deviation control techniques. International Journal of Innovative Computing, Information and Control, 14(4), 1407-1421.
Lin, T.-K (2014). An adaptive vision-based method for automated inspection in manufacturing. Advances in Mechanical Engineering, ID: 616341, 1-7.
Lin, Y., Xiang, Y., Lin, Y., & Yu, J. (2019). Defect detection system for optical element surface based on machine vision. in: 2019 IEEE 2nd International Conference on Information Systems and Computer Aided Education, 415-418.
Liu, S., Li, Z., Zhong, K., Chao, Y.J., Miraldo, P., & Shi, Y. (2018). Generic distortion model for metrology under optical microscopes. Optics and Lasers in Engineering, 103, 119-126.
Mansouri, M., Harkat, M.F., Nounou, M., & Nounou, H. (2018). Midpoint-radii principal component analysis -based EWMA and application to air quality monitoring network. Chemometrics and Intelligent Laboratory Systems, 175, 55-64.
Mantel, C., Villebro, F., Parikh, H.R., Spataru, S., dos Reis Benatto, G.A., Sera, D., Poulsen, P.B., & Forchhammer, S. (2020). Method for estimation and correction of perspective distortion of electroluminescence images of photovoltaic panels. IEEE Journal of Photovoltaics, 10(6), 1797-1802.
Mitra, A., Lee, K.B., & Chakraborti, S. (2019). An adaptive exponentially weighted moving average-type control chart to monitor the process mean. European Journal of Operational Research, 279(3), 902-911.
Montgomery, D.C. (2019). Introduction to Statistical Quality Control, 8th Edition, John Wiley & Sons, New York, NY, USA.
Otsu, N. (1979). A threshold selection method from gray level histogram. IEEE Transactions on Systems, Man and Cybernetics, 9, 62-66.
Sanusi, R.A., Riaz, M., Adegoke, N.A., & Xie, M. (2017). An EWMA monitoring scheme with a single auxiliary variable for industrial processes. Computers & Industrial Engineering, 114, 1-10.
Shihabudheen, K.V., & Pillai, G.N. (2018). Recent advances in neuro-fuzzy system: A survey. Knowledge-Based Systems, 152, 136-162.
Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15(1) 116-132.
Tan, K.L., Ooi, B.C., & Thiang, L.F. (2000). Indexing shapes in image databases using the centroid-radii model. Data & Knowledge Engineering, 32, 271-289.
Tulbure, A.-A., Tulbure, A.-A., & Dulf, E.-H. (2022). A review on modern defect detection models using DCNNs-Deep convolutional neural networks. Journal of Advanced Research, 35, 33-48.
Vahid, S., Farshid, F.A., & Hamid, E. (2018). A new fuzzy measurement approach for automatic change detection using remotely sensed images. Measurement, 127, 1-14.
Vishal, K., Ashwani, K., Deepak, C., & Pratyoosh, S. (2019). Improved biobleaching of mixed hardwood pulp and process optimization using novel GA-ANN and GA-ANFIS hybrid statistical tools. Bioresource Technology, 271, 274-282.
Xu, J., Xi, N., Zhang, C., Shi, Q., & Gregory, J. (2010). A geometry and optical property inspection system for automotive glass based on fringe patterns. Optica Applicata, 40(4), 827-841.
Yao, H.B., Ping, J., Ma, G.D., Li, L.W., & Gu, J.N. (2013). The system research on automatic defect detection of glasses. Applied Mechanics and Materials, 437, 362-365.
Youngquist, R.C., Skow, M., & Nurgle, M.A. (2015). Optical distortion evaluation in large area windows using interferometry. in: 14th International Symposium on Nondestructive Characterization of Materials, Marina Del Rey, California, USA.
Zhang, T.Y., Suen, C.Y. (1984). A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 27(3), 236-239.
Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., & Shao, L. (2021). Surface defect detection methods for industrial products: a review. Applied Sciences, 11(16), 7657.
Chiu, Y.-S. P., & Lin, H.-D. (2013). An innovative blemish detection system for curved LED lenses. Expert Systems with Applications, 40(2), 471-479.
Cutolo, F., Fontana, U., Cattari, N., & Ferrari, V. (2020). Off-line camera-based calibration for optical see-through head-mounted displays. Applied Sciences, 10(193), 1-19.
Daniel, S.C., Luis, G., Miguel, A.F., Agustin, S., Julio, E., Luis, M., & Luis, A. (2017). Automatic correction of perspective and optical distortions. Computer Vision and Image Understanding, 161, 1-10.
Dixon, M., Glaubius, R., Freeman, P., Pless, R., Gleason, M.P., Thomas, M.M., & Smart, W. (2011). Measuring optical distortion in aircraft transparencies: a fully automated system for quantitative evaluation. Machine Vision and Applications, 22(5), 791-804.
Gerton, K.M., Novar, B.J., Brockmeier, W., & Putnam, C. (2019). A novel method for optical distortion quantification. Optometry and Vision Science, 96(2), 117-123.
Hou, Y., Zhang, H., Zhao, J., Jian, H., Hao, Q., Liu, Z., & Guo, B. (2018). Camera lens distortion evaluation and correction technique based on a colour CCD moiré method. Optics and Lasers in Engineering, 110, 211-219.
Jang, J.S.R. (1993). ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.
Karangwa, J., Kong, L.H., Yi, D.R., & Zheng, J.S. (2021). Automatic optical inspection platform for real-time surface defects detection on plane optical components based on semantic segmentation. Applied Optics, 60(19), 5496-5506.
Karaboga, D., & Kaya, E. (2018). Adaptive network-based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artificial Intelligence Review, 52(4), 2263-2293.
Kuo, C.-F. J., Lo, W.-C., Huang, Y.-R., Tsai, H.-Y., Lee, C.-L., & Wu, H.-C. (2017). Automated defect inspection system for CMOS image sensor with micro multi-layer non-spherical lens module. Journal of Manufacturing Systems, 45, 248-259.
Le, N.T., Wang, J.-W., Wang, C.-C., & Nguyen, T.N. (2019). Automatic defect inspection for coated eyeglass based on symmetrized energy analysis of color channels. Symmetry, 11, 1518.
Li, K., & Su, H. (2010). Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system. Energy and Buildings, 42, 2070-2076.
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.
Lin, H.-D., & Lo, Y.-C. (2016). Automated distortion defect inspection of transparent glass using computer vision. Proceedings of the 2016 International Conference on Image Processing, Computer Vision, and Pattern Recognition, 235-240.
Lin, H.-D., & Hsieh, K.-S. (2018). Detection of surface variations on curved mirrors of vehicles using slight deviation control techniques. International Journal of Innovative Computing, Information and Control, 14(4), 1407-1421.
Lin, T.-K (2014). An adaptive vision-based method for automated inspection in manufacturing. Advances in Mechanical Engineering, ID: 616341, 1-7.
Lin, Y., Xiang, Y., Lin, Y., & Yu, J. (2019). Defect detection system for optical element surface based on machine vision. in: 2019 IEEE 2nd International Conference on Information Systems and Computer Aided Education, 415-418.
Liu, S., Li, Z., Zhong, K., Chao, Y.J., Miraldo, P., & Shi, Y. (2018). Generic distortion model for metrology under optical microscopes. Optics and Lasers in Engineering, 103, 119-126.
Mansouri, M., Harkat, M.F., Nounou, M., & Nounou, H. (2018). Midpoint-radii principal component analysis -based EWMA and application to air quality monitoring network. Chemometrics and Intelligent Laboratory Systems, 175, 55-64.
Mantel, C., Villebro, F., Parikh, H.R., Spataru, S., dos Reis Benatto, G.A., Sera, D., Poulsen, P.B., & Forchhammer, S. (2020). Method for estimation and correction of perspective distortion of electroluminescence images of photovoltaic panels. IEEE Journal of Photovoltaics, 10(6), 1797-1802.
Mitra, A., Lee, K.B., & Chakraborti, S. (2019). An adaptive exponentially weighted moving average-type control chart to monitor the process mean. European Journal of Operational Research, 279(3), 902-911.
Montgomery, D.C. (2019). Introduction to Statistical Quality Control, 8th Edition, John Wiley & Sons, New York, NY, USA.
Otsu, N. (1979). A threshold selection method from gray level histogram. IEEE Transactions on Systems, Man and Cybernetics, 9, 62-66.
Sanusi, R.A., Riaz, M., Adegoke, N.A., & Xie, M. (2017). An EWMA monitoring scheme with a single auxiliary variable for industrial processes. Computers & Industrial Engineering, 114, 1-10.
Shihabudheen, K.V., & Pillai, G.N. (2018). Recent advances in neuro-fuzzy system: A survey. Knowledge-Based Systems, 152, 136-162.
Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15(1) 116-132.
Tan, K.L., Ooi, B.C., & Thiang, L.F. (2000). Indexing shapes in image databases using the centroid-radii model. Data & Knowledge Engineering, 32, 271-289.
Tulbure, A.-A., Tulbure, A.-A., & Dulf, E.-H. (2022). A review on modern defect detection models using DCNNs-Deep convolutional neural networks. Journal of Advanced Research, 35, 33-48.
Vahid, S., Farshid, F.A., & Hamid, E. (2018). A new fuzzy measurement approach for automatic change detection using remotely sensed images. Measurement, 127, 1-14.
Vishal, K., Ashwani, K., Deepak, C., & Pratyoosh, S. (2019). Improved biobleaching of mixed hardwood pulp and process optimization using novel GA-ANN and GA-ANFIS hybrid statistical tools. Bioresource Technology, 271, 274-282.
Xu, J., Xi, N., Zhang, C., Shi, Q., & Gregory, J. (2010). A geometry and optical property inspection system for automotive glass based on fringe patterns. Optica Applicata, 40(4), 827-841.
Yao, H.B., Ping, J., Ma, G.D., Li, L.W., & Gu, J.N. (2013). The system research on automatic defect detection of glasses. Applied Mechanics and Materials, 437, 362-365.
Youngquist, R.C., Skow, M., & Nurgle, M.A. (2015). Optical distortion evaluation in large area windows using interferometry. in: 14th International Symposium on Nondestructive Characterization of Materials, Marina Del Rey, California, USA.
Zhang, T.Y., Suen, C.Y. (1984). A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 27(3), 236-239.