{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:02:47Z","timestamp":1772301767944,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,14]],"date-time":"2019-12-14T00:00:00Z","timestamp":1576281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>No matter your experience level or budget, there is a great ski goggle waiting to be found.Goggles are an essential part of skiing or snowboarding gear to protect your eyes from harsh environmental elements and injury. In the ski goggles manufacturing industry, defects, especially on the lens surface, are unavoidable. However, defect detection and classification by visual inspection in the manufacturing process is very difficult. To overcome this problem, a novel framework based on machine vision is presented, named as the ski goggles lens defect detection, with five high-resolution cameras and custom-made lighting field to achieve a high-quality ski goggles lens image. Next, the defects on the lens of ski goggles are detected by using parallel projection in opposite directions based on adaptive energy analysis. Before being put into the classification system, the defect images are enhanced by an adaptive method based on the high-order singular value decomposition (HOSVD). Finally, dust and five types of defect images are classified into six types, i.e., dust, spotlight (type 1, type 2, type 3), string, and watermark, by using the developed classification algorithm. The defect detection and classification results of the ski goggles lens are compared to the standard quality of the manufacturer. Experiments using 120 ski goggles lens samples collected from the largest manufacturer in Taiwan are conducted to validate the performance of the proposed framework. The accurate defect detection rate is 100% and the classification accuracy rate is 99.3%, while the total running time is short. The results demonstrate that the proposed method is sound and useful for ski goggles lens inspection in industries.<\/jats:p>","DOI":"10.3390\/s19245538","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T05:19:38Z","timestamp":1576473578000},"page":"5538","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Novel Framework Based on HOSVD for Ski Goggles Defect Detection and Classification"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5155-2150","authenticated-orcid":false,"given":"Ngoc Tuyen","family":"Le","sequence":"first","affiliation":[{"name":"Institute of Photonics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8585-642X","authenticated-orcid":false,"given":"Jing-Wein","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Photonics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3960-3856","authenticated-orcid":false,"given":"Chou-Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, I-Shou University, Kaohsiung 84001, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7184-4102","authenticated-orcid":false,"given":"Tu N.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Purdue University Fort Wayne, IN 46805, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10817","DOI":"10.1109\/ACCESS.2016.2631658","article-title":"A machine vision-based automatic optical inspection system for measuring drilling quality of printed circuit boards","volume":"5","author":"Wang","year":"2016","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1109\/TIM.2013.2283741","article-title":"Automatic fastener classification and defect detection in vision-based railway inspection systems","volume":"63","author":"Feng","year":"2014","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"027205","DOI":"10.1117\/1.3544588","article-title":"Inline inspection of textured plastics surfaces","volume":"50","author":"Michaeli","year":"2011","journal-title":"Opt. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"053108","DOI":"10.1117\/1.OE.56.5.053108","article-title":"Vision-based surface defect inspection for thick steel plates","volume":"56","author":"Yun","year":"2017","journal-title":"Opt. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.rcim.2015.09.008","article-title":"A cost-effective and automatic surface defect inspection system for hot-rolled flat steel","volume":"38","author":"Lou","year":"2016","journal-title":"Rob. Comput. Integr. Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4253","DOI":"10.1109\/TIM.2018.2886977","article-title":"Automated visual inspection of glass bottle bottom with saliency detection and template matching","volume":"68","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4030","DOI":"10.1016\/j.ijleo.2012.12.024","article-title":"Optical inspection system with tunable exposure unit for micro-crack detection in solar wafers","volume":"124","author":"Ko","year":"2013","journal-title":"Optik"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.aei.2015.01.014","article-title":"Defect detection in multi-crystal solar cells using clustering with uniformity measures","volume":"29","author":"Tsai","year":"2015","journal-title":"Adv. Eng. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1049\/ip-vis:20045131","article-title":"Robust fabric defect detection and classification using multiple adaptive wavelets","volume":"152","author":"Yang","year":"2005","journal-title":"IEE Proc. Vis. Image. Sign."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.1109\/TII.2017.2668438","article-title":"Accurate and efficient inspection of speckle and scratch defects on surfaces of planar products","volume":"13","author":"Kong","year":"2017","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1016\/j.proeng.2011.08.334","article-title":"Research on in-line glass defect inspection technology based on dual CCFL","volume":"15","author":"Jin","year":"2011","journal-title":"Procedia Eng."},{"key":"ref_12","unstructured":"Adamo, F., Attivissimo, F., Di Nissio, A., and Savino, M. (2008, January 22\u201324). An automated visual inspection system for the glass industry. Proceedings of the 16th IMEKO TC4 Symposium, Florence, Italy."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.optlaseng.2013.11.013","article-title":"Automatic inspection system of surface defects on optical IR-CUT filter based on machine vision","volume":"55","author":"Liu","year":"2014","journal-title":"Opt. Laser Eng."},{"key":"ref_14","unstructured":"Gonzalez, R.C., and Woods, R.E. (2008). Digital Image Processing, Prentice-Hall. [3rd ed.]."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1016\/S0031-3203(03)00083-9","article-title":"Neuro-fuzzy synergism to the intelligent system for edge detection and enhancement","volume":"36","author":"Lu","year":"2003","journal-title":"Pattern Recognit."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.patrec.2007.10.021","article-title":"Edge detection improvement by ant colony optimization","volume":"29","author":"Lu","year":"2008","journal-title":"Pattern Recogn. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1109\/83.597279","article-title":"A study of efficiency and accuracy in the transformation from RGB to CIELAB color space","volume":"6","author":"Connolly","year":"1997","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/LSP.2008.926729","article-title":"Pose invariant face recognition using probability distribution functions in different color channels","volume":"15","author":"Demirel","year":"2008","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1109\/LSP.2011.2163798","article-title":"Face recognition based on projected color space with lighting compensation","volume":"18","author":"Wang","year":"2011","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, J.W., Le, N.T., Lee, J.S., and Chen, W.Y. (2014). Recognition based on two separated singular value decomposition-enriched faces. J. Electron. Imaging, 23.","DOI":"10.1117\/1.JEI.23.6.063010"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.patcog.2016.03.021","article-title":"Color face image enhancement using adaptive singular value decomposition in the Fourier domain for face recognition","volume":"57","author":"Wang","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.ins.2017.12.057","article-title":"Illumination compensation for face recognition using adaptive singular value decomposition in the wavelet domain","volume":"435","author":"Wang","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_24","unstructured":"Tucker, L.R. (1963). Implications of factor analysis of three-way matrices for measurement of change. Problem in Measuring Change, The University of Wisconsin Press."},{"key":"ref_25","unstructured":"Tucker, L.R. (1964). The extension of factor analysis to three-dimensional matrices. Handbook of Mathematical Psychology, Holt, Rinehart and Winston."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/BF02289464","article-title":"Some mathematical notes on three-mode factor analysis","volume":"31","author":"Tucker","year":"1966","journal-title":"Psychometrika"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Muralidhara, C., Gross, A.M., Gutell, R.R., and Alter, O. (2011). Tensor decomposition reveals concurrent evolutionary convergences and divergences and correlations with structural Motifs in Ribosomal RNA. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0018768"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Vasilescu, M.A.O., and Terzopoulos, D. (2004, January 8\u201312). Tensor Textures: Multilinear image-based rendering. Proceedings of the ACM SIGGRAPH 2004 Conference, Los Angeles, CA, USA.","DOI":"10.1145\/1186562.1015725"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Vasilescu, M.A.O., and Terzopoulos, D. (2002, January 28\u201331). Multilinear analysis of image ensembles: Tensor faces. Proceedings of the 7th European Conference on Computer Vision (ECCV 2002), Copenhagen, Denmark.","DOI":"10.1007\/3-540-47969-4_30"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1137\/07070111X","article-title":"Tensor decomposition and applications","volume":"51","author":"Kolda","year":"2009","journal-title":"SIAM Rev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1007\/s10278-007-9044-5","article-title":"Information entropy measure for evaluation of image quality","volume":"21","author":"Tsai","year":"2008","journal-title":"J. Digit Imaging"},{"key":"ref_32","first-page":"113","article-title":"A novel method of determining parameters of CLAHE based on image entropy","volume":"7","author":"Min","year":"2013","journal-title":"Int. J. Softw. Eng. Its Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_34","unstructured":"Ivezi\u2019c, \u017d., Andrew, J.C., Jacob, V.P.T., and Alexander, G. (2019). Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Princeton University Press."},{"key":"ref_35","unstructured":"Imad, R., Noor, A.M., Somaya, A.M., and Sambit, B. (2018). A Comprehensive Overview of Feature Representation for Biometric Recognition. Multimedia Tools and Applications, Springer."},{"key":"ref_36","first-page":"423","article-title":"Multimodal machine learning: A survey and taxonomy","volume":"41","author":"Ahuja","year":"2018","journal-title":"IEEE Trans.Patt Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3241","DOI":"10.1109\/ACCESS.2017.2787666","article-title":"Palmprint identification using an ensemble of sparse representations","volume":"6","author":"Rida","year":"2018","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution grayscale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Patt Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2599","DOI":"10.1109\/TIP.2009.2028255","article-title":"Face recognition under varying illumination using Gradientfaces","volume":"18","author":"Zhang","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1109\/LSP.2011.2158998","article-title":"Illumination normalization based on Weber\u2019s law with application to face recognition","volume":"18","author":"Wang","year":"2011","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortesand","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_42","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the Computer Vision and Pattern Recognition, San Diego, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/24\/5538\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:42:27Z","timestamp":1760190147000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/24\/5538"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,14]]},"references-count":42,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19245538"],"URL":"https:\/\/doi.org\/10.3390\/s19245538","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,14]]}}}