{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T02:16:04Z","timestamp":1773886564841,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council (R.O.C.)","award":["MOST 104-2221-E-324-010"],"award-info":[{"award-number":["MOST 104-2221-E-324-010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Multifocal glasses are a new type of lens that can fit both nearsighted and farsighted vision on the same lens. This property allows the glass to have various curvatures in distinct regions within the glass during the grinding process. However, when the curvature varies irregularly, the glass is prone to optical deformation during imaging. Most of the previous studies on imaging deformation focus on the deformation correction of optical lenses. Consequently, this research uses an automatic deformation defect detection system for multifocal glasses to replace professional assessors. To quantify the grade of deformation of curved multifocal glasses, we first digitally imaged a pattern of concentric circles through a test glass to generate an imaged image of the glass. Second, we preprocess the image to enhance the clarity of the concentric circles\u2019 appearance. A centroid-radius model is used to represent the form variation properties of every circle in the processed image. Third, the deviation of the centroid radius for detecting deformation defects is found by a slight deviation control scheme, and we gain a difference image indicating the detected deformed regions after comparing it with the norm pattern. Fourth, based on the deformation measure and occurrence location of multifocal glasses, we build fuzzy membership functions and inference regulations to quantify the deformation\u2019s severity. Finally, a mixed model incorporating a network-based fuzzy inference and a genetic algorithm is applied to determine a quality grade for the deformation severity of detected defects. Testing outcomes show that the proposed methods attain a 94% accuracy rate of the quality levels for deformation severity, an 81% recall rate of deformation defects, and an 11% false positive rate for multifocal glass detection. This research contributes solutions to the problems of imaging deformation inspection and provides computer-aided systems for determining quality levels that meet the demands of inspection and quality control.<\/jats:p>","DOI":"10.3390\/s23094497","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T03:57:31Z","timestamp":1683259051000},"page":"4497","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Optical Imaging Deformation Inspection and Quality Level Determination of Multifocal Glasses"],"prefix":"10.3390","volume":"23","author":[{"given":"Hong-Dar","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan"}]},{"given":"Tung-Hsin","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan"}]},{"given":"Chou-Hsien","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, Austin, TX 78712-0273, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3663-6284","authenticated-orcid":false,"given":"Hsin-Chieh","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhou, W., Shao, Z., Yu, J., and Lin, J. (2021). Advances and Trends in Forming Curved Extrusion Profiles. Materials, 14.","DOI":"10.3390\/ma14071603"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.csndt.2014.03.003","article-title":"The inspection of curved components using flexible ultrasonic arrays and shape sensing fibres","volume":"1","author":"Lane","year":"2014","journal-title":"Case Stud. Nondestruct. Test. Eval."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109522","DOI":"10.1016\/j.ymssp.2022.109522","article-title":"Ultrasonic full-matrix imaging of curved-surface components","volume":"181","author":"Ji","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.cad.2013.10.011","article-title":"A variational-difference numerical method for designing progressive-addition lenses","volume":"48","author":"Jiang","year":"2014","journal-title":"Comput.-Aided Des."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/S0010-4485(97)00102-4","article-title":"A variational approach to progressive lens design","volume":"30","author":"Loos","year":"1998","journal-title":"Comput.-Aided Des."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s40684-021-00343-6","article-title":"State of the art in defect detection based on machine vision","volume":"9","author":"Ren","year":"2022","journal-title":"Int. J. Precis. Eng. Manuf.-Green Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107722","DOI":"10.1016\/j.measurement.2020.107722","article-title":"A comprehensive review of defect detection in 3C glass components","volume":"158","author":"Ming","year":"2020","journal-title":"Measurement"},{"key":"ref_8","first-page":"100134","article-title":"Deep learning in computer vision: A critical review of emerging techniques and application scenarios","volume":"6","author":"Chai","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lin, H.-D., Tsai, H.-H., Lin, C.-H., and Chang, H.-T. (2023). Optical Panel Inspection Using Explicit Band Gaussian Filtering Methods in Discrete Cosine Domain. Sensors, 23.","DOI":"10.3390\/s23031737"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.jmsy.2017.10.004","article-title":"Automated defect inspection system for CMOS image sensor with micro multi-layer non-spherical lens module","volume":"45","author":"Kuo","year":"2017","journal-title":"J. Manuf. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"90213","DOI":"10.1109\/ACCESS.2022.3201340","article-title":"Incorporating Visual Defect Identification and Determination of Occurrence Side in Touch Panel Quality Inspection","volume":"10","author":"Lin","year":"2022","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"279","DOI":"10.22201\/icat.24486736e.2021.19.4.958","article-title":"Optical inspection of appearance faults for auto mirrors using Fourier filtering and convex hull arithmetic","volume":"19","author":"Chiu","year":"2021","journal-title":"J. Appl. Res. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2017.05.016","article-title":"Automatic correction of perspective and optical distortions","volume":"161","author":"Gomez","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/JPHOTOV.2020.3019949","article-title":"Method for Estimation and Correction of Perspective Distortion of Electroluminescence Images of Photovoltaic Panels","volume":"10","author":"Mantel","year":"2020","journal-title":"IEEE J. Photovolt."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cutolo, F., Fontana, U., Cattari, N., and Ferrari, V. (2019). Off-Line Camera-Based Calibration for Optical See-Through Head-Mounted Displays. Appl. Sci., 10.","DOI":"10.3390\/app10010193"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.optlaseng.2018.06.008","article-title":"Camera lens distortion evaluation and correction technique based on a colour CCD moir\u00e9 method","volume":"110","author":"Hou","year":"2018","journal-title":"Opt. Lasers Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.optlaseng.2017.12.006","article-title":"Generic distortion model for metrology under optical microscopes","volume":"103","author":"Liu","year":"2018","journal-title":"Opt. Lasers Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s00138-010-0258-z","article-title":"Measuring optical distortion in aircraft transparencies: A fully automated system for quantitative evaluation","volume":"22","author":"Dixon","year":"2010","journal-title":"Mach. Vis. Appl."},{"key":"ref_19","unstructured":"Youngquist, R.C., Skow, M., and Nurge, M.A. (2015, January 22\u201326). Optical distortion evaluation in large area windows using interferometry. Proceedings of the 14th International Symposium on Nondestructive Characterization of Materials, Marina Del Rey, CA, USA."},{"key":"ref_20","first-page":"483","article-title":"Effective mathematical schemes for measuring the surface distortions of curved mirrors with applications","volume":"103","author":"Chiu","year":"2018","journal-title":"Far East J. Math. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1097\/OPX.0000000000001335","article-title":"A Novel Method for Optical Distortion Quantification","volume":"96","author":"Gerton","year":"2019","journal-title":"Optom. Vis. Sci."},{"key":"ref_22","first-page":"1217","article-title":"Computer-aided transmitted deformation inspection system for see-through glass products","volume":"18","author":"Lin","year":"2022","journal-title":"Int. J. Innov. Comput. Inf. Control"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Le, N.T., Wang, J.-W., Wang, C.-C., and Nguyen, T.N. (2019). Automatic Defect Inspection for Coated Eyeglass Based on Symmetrized Energy Analysis of Color Channels. Symmetry, 11.","DOI":"10.3390\/sym11121518"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"362","DOI":"10.4028\/www.scientific.net\/AMM.437.362","article-title":"The System Research on Automatic Defect Detection of Glasses","volume":"437","author":"Yao","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5496","DOI":"10.1364\/AO.424547","article-title":"Automatic optical inspection platform for real-time surface defects detection on plane optical components based on semantic segmentation","volume":"60","author":"Karangwa","year":"2021","journal-title":"Appl. Opt."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, Y., Xiang, Y., Lin, Y., and Yu, J. (2019, January 28\u201330). Defect detection system for optical element surface based on machine vision. Proceedings of the 2019 IEEE 2nd International Conference on Information Systems and Computer Aided Education, Dalian, China.","DOI":"10.1109\/ICISCAE48440.2019.221665"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"616341","DOI":"10.1155\/2014\/616341","article-title":"An Adaptive Vision-Based Method for Automated Inspection in Manufacturing","volume":"6","author":"Lin","year":"2014","journal-title":"Adv. Mech. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1117\/1.1631315","article-title":"Survey over image thresholding techniques and quantitative performance evaluation","volume":"13","author":"Sezgin","year":"2004","journal-title":"J. Electron. Imaging"},{"key":"ref_29","unstructured":"Gonzalez, R.C., and Woods, R.E. (2018). Digital Image Processing, Pearson. [4th ed.]."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.optcom.2007.01.044","article-title":"A new shape descriptor based on centroid\u2013radii model and wavelet transform","volume":"273","author":"Kong","year":"2007","journal-title":"Opt. Commun."},{"key":"ref_31","unstructured":"Montgomery, D.C. (2013). Statistical Quality Control: A Modern Introduction, John Wiley & Sons Singapore Pte. Ltd.. [7th ed.]."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.microrel.2010.07.151","article-title":"Using EWMA control schemes for monitoring wafer quality in negative binomial process","volume":"51","author":"Yu","year":"2011","journal-title":"Microelectron. Reliab."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/00401706.1990.10484583","article-title":"Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements","volume":"32","author":"Lucas","year":"1990","journal-title":"Technometrics"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sukparungsee, S., Areepong, Y., and Taboran, R. (2020). Exponentially weighted moving average\u2014Moving average charts for monitoring the process mean. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0228208"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1016\/j.cie.2004.05.025","article-title":"Comparative study of the performance of the CuSum and EWMA control charts","volume":"46","author":"Lopes","year":"2004","journal-title":"Comput. Ind. Eng."},{"key":"ref_36","first-page":"73","article-title":"A study on the effects of trends due to inertia on EWMA and CUSUM charts","volume":"5","author":"Khoo","year":"2009","journal-title":"J. Qual. Meas. Anal."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s00521-017-2862-6","article-title":"Fuzzy logic-based segmentation of manufacturing defects on reflective surfaces","volume":"29","author":"Akdemir","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s10462-022-10188-3","article-title":"Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: A systematic survey","volume":"56","author":"Talpur","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TSMC.1985.6313399","article-title":"Fuzzy Identification of Systems and Its Applications to Modeling and Control","volume":"15","author":"Takagi","year":"1985","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/21.256541","article-title":"ANFIS: Adaptive-Network-Based Fuzzy Inference System","volume":"23","author":"Jang","year":"1993","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_41","first-page":"343","article-title":"A Survey on Applications of Adaptive Neuro Fuzzy Inference System","volume":"8","author":"Walia","year":"2015","journal-title":"Int. J. Hybrid Inf. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1016\/j.eswa.2010.08.010","article-title":"An adaptive network based fuzzy inference system\u2013genetic algorithm clustering ensemble algorithm for performance assessment and improvement of conventional power plants","volume":"38","author":"Azadeh","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Olayode, I.O., Tartibu, L.K., and Alex, F.J. (2023). Comparative Study Analysis of ANFIS and ANFIS-GA Models on Flow of Vehicles at Road Intersections. Appl. Sci., 13.","DOI":"10.3390\/app13020744"},{"key":"ref_44","first-page":"37","article-title":"Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation","volume":"2","author":"Powers","year":"2011","journal-title":"J. Mach. Learn. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1111\/2041-210X.13140","article-title":"The area under the precision-recall curve as a performance metric for rare binary events","volume":"10","author":"Sofaer","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Brodersen, K.H., Ong, C.S., Stephan, K.E., and Buhmann, J.M. (2010, January 23\u201326). The binormal assumption on precision-recall curves. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.1036"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1177\/1536867X20909693","article-title":"When to consult precision-recall curves","volume":"20","author":"Cook","year":"2020","journal-title":"Stata J. Promot. Commun. Stat. Stata"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4497\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:29:36Z","timestamp":1760124576000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4497"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,5]]},"references-count":48,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094497"],"URL":"https:\/\/doi.org\/10.3390\/s23094497","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,5]]}}}