{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:51:15Z","timestamp":1760147475875,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001868","name":"National Science Council of Taiwan","doi-asserted-by":"publisher","award":["NSC 101-2221-E-324-007-MY2"],"award-info":[{"award-number":["NSC 101-2221-E-324-007-MY2"]}],"id":[{"id":"10.13039\/501100001868","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Capacitive touch panels (CTPs) have the merits of being waterproof, antifouling, scratch resistant, and capable of rapid response, making them more popular in various touch electronic products. However, the CTP has a multilayer structure, and the background is a directional texture. The inspection work is more difficult when the defect area is small and occurs in the textured background. This study focused mainly on the automated defect inspection of CTPs with structural texture on the surface, using the spectral attributes of the discrete cosine transform (DCT) with the proposed three-way double-band Gaussian filtering (3W-DBGF) method. With consideration to the bandwidth and angle of the high-energy region combined with the characteristics of band filtering, threshold filtering, and Gaussian distribution filtering, the frequency values with higher energy are removed, and after reversal to the spatial space, the textured background can be weakened and the defects enhanced. Finally, we use simple statistics to set binarization threshold limits that can accurately separate defects from the background. The detection outcomes showed that the flaw detection rate of the DCT-based 3W-DBGF approach was 94.21%, the false-positive rate of the normal area was 1.97%, and the correct classification rate was 98.04%.<\/jats:p>","DOI":"10.3390\/s23031737","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T02:06:43Z","timestamp":1675649203000},"page":"1737","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Optical Panel Inspection Using Explicit Band Gaussian Filtering Methods in Discrete Cosine Domain"],"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":"Huan-Hua","family":"Tsai","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"}]},{"given":"Hung-Tso","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"183192","DOI":"10.1109\/ACCESS.2020.3029127","article-title":"A review and analysis of automatic optical inspection and quality monitoring methods in electronic industry","volume":"8","author":"Ebayyeh","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/TIM.2019.2963555","article-title":"Automated Visual Defect Detection for Flat Steel Surface: A Survey","volume":"69","author":"Luo","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1016\/j.measurement.2009.05.006","article-title":"A low-cost inspection system for online defects assessment in satin glass","volume":"42","author":"Adamo","year":"2009","journal-title":"Measurement"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1016\/j.patrec.2006.03.009","article-title":"Automatic thresholding for defect detection","volume":"27","author":"Ng","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1016\/j.patcog.2011.07.025","article-title":"Wavelet-based defect detection in solar wafer images with inhomogeneous texture","volume":"45","author":"Li","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.imavis.2007.07.009","article-title":"Tiny surface defect inspection of electronic passive components using discrete cosine transform decomposition and cumulative sum techniques","volume":"26","author":"Lin","year":"2008","journal-title":"Image Vis. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lettry, L., Perdoch, M., Vanhoey, K., and Van Gool, L. (2017, January 24\u201331). Repeated pattern detection using CNN activations. Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA, USA.","DOI":"10.1109\/WACV.2017.13"},{"key":"ref_9","first-page":"291","article-title":"Automated quality inspection of surface defects on touch panels","volume":"29","author":"Lin","year":"2012","journal-title":"J. Chin. Inst. Ind. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.imavis.2015.06.001","article-title":"A novel algorithm for flaw inspection of touch panels","volume":"41","author":"Hung","year":"2015","journal-title":"Image Vis. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2655","DOI":"10.1007\/s11042-015-2559-8","article-title":"Touch screen flaw inspection based on sparse representation in low-resolution images","volume":"75","author":"Liang","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"333","DOI":"10.5937\/jaes16-16888","article-title":"Creation of image models for inspecting visual flaws on capacitive touch screens","volume":"16","author":"Chiu","year":"2018","journal-title":"J. Appl. Eng. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.asoc.2016.10.030","article-title":"Automatic surface flaw detection for mobile phone screen glass based on machine vision","volume":"52","author":"Jian","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/1687814018766682","article-title":"Intelligent flaw classification system based on deep learning","volume":"10","author":"Ye","year":"2018","journal-title":"Adv. Mech. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.neucom.2018.03.013","article-title":"Scale insensitive and focus driven mobile screen flaw detection in industry","volume":"294","author":"Lei","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15599612.2018.1444829","article-title":"High-resolution optical inspection system for fast detection and classification of surface flaws","volume":"21","author":"Ye","year":"2018","journal-title":"Int. J. Optomechatronics"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S0262-8856(99)00009-8","article-title":"Automated surface inspection for directional textures","volume":"18","author":"Tsai","year":"1999","journal-title":"Image Vis. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s00170-009-2294-2","article-title":"Automatic surface inspection for directional textures using nonnegative matrix factorization","volume":"48","author":"Perng","year":"2010","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_19","first-page":"5141","article-title":"Automated optical inspection system for analogical resistance type touch panel","volume":"6","author":"Chen","year":"2011","journal-title":"Int. J. Phys. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1364\/AO.55.002331","article-title":"Defect detection of capacitive touch panel using a nonnegative matrix factorization and tolerance model","volume":"55","author":"Jiang","year":"2016","journal-title":"Appl. Opt."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/T-C.1974.223784","article-title":"Discrete cosine transform","volume":"23","author":"Ahmed","year":"1974","journal-title":"IEEE Trans. Comput."},{"key":"ref_22","unstructured":"Gonzalez, R.C., and Woods, R.E. (2018). Digital Image Processing, Pearson. [4th ed.]."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"043020","DOI":"10.1117\/1.JEI.21.4.043020","article-title":"Wiener discrete cosine transform-based image filtering","volume":"21","author":"Pogrebnyak","year":"2012","journal-title":"J. Electron. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.acha.2015.08.008","article-title":"A new auto-focus measure based on medium frequency discrete cosine transform filtering and discrete cosine transform","volume":"40","author":"Zhang","year":"2016","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/LSP.2020.2966888","article-title":"A Deep Learning Approach in the Discrete Cosine Transform Domain to Median Filtering Forensics","volume":"27","author":"Zhang","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_26","first-page":"458","article-title":"Applying discrete cosine transform and grey relational analysis to surface defect detection of LEDs","volume":"24","author":"Lin","year":"2007","journal-title":"J. Chin. Inst. Ind. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1737\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:23:51Z","timestamp":1760120631000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1737"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,3]]},"references-count":26,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031737"],"URL":"https:\/\/doi.org\/10.3390\/s23031737","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,2,3]]}}}