{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T20:52:07Z","timestamp":1757451127322,"version":"3.37.3"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s00371-024-03358-7","type":"journal-article","created":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T14:01:35Z","timestamp":1713103295000},"page":"723-737","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Automated fabric defect detection using multi-scale fusion MemAE"],"prefix":"10.1007","volume":"41","author":[{"given":"Kun","family":"Wu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7001-5775","authenticated-orcid":false,"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Weihang","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Wenwu","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,14]]},"reference":[{"issue":"24","key":"3358_CR1","doi-asserted-by":"publisher","first-page":"11960","DOI":"10.1016\/j.ijleo.2016.09.110","volume":"127","author":"K Hanbay","year":"2016","unstructured":"Hanbay, K., Talu, M.F., \u00d6zg\u00fcven, \u00d6.F.: Fabric defect detection systems and methods-a systematic literature review. Optik 127(24), 11960\u201311973 (2016)","journal-title":"Optik"},{"key":"3358_CR2","first-page":"1","volume":"2021","author":"C Li","year":"2021","unstructured":"Li, C., Li, J., Li, Y., He, L., Fu, X., Chen, J.: Fabric defect detection in textile manufacturing: a survey of the state of the art. Secur. Commun. Netw. 2021, 1\u201313 (2021)","journal-title":"Secur. Commun. Netw."},{"key":"3358_CR3","doi-asserted-by":"crossref","first-page":"5947","DOI":"10.1109\/ACCESS.2017.2675940","volume":"5","author":"L Tong","year":"2017","unstructured":"Tong, L., Wong, W.K., Kwong, C.K.: Fabric defect detection for apparel industry: a nonlocal sparse representation approach. IEEE Access 5, 5947\u20135964 (2017)","journal-title":"IEEE Access"},{"issue":"1","key":"3358_CR4","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s11042-017-5263-z","volume":"78","author":"P Li","year":"2019","unstructured":"Li, P., Liang, J., Shen, X., Zhao, M., Sui, L.: Textile fabric defect detection based on low-rank representation. Multimed. Tools Appl. 78(1), 99\u2013124 (2019)","journal-title":"Multimed. Tools Appl."},{"issue":"11","key":"3358_CR5","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"3358_CR6","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097\u20131105 (2012)"},{"key":"3358_CR7","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Huang, F.J., Bottou, L.: Learning methods for generic object recognition with invariance to pose and lighting. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, p. 104. IEEE (2004)","DOI":"10.1109\/CVPR.2004.1315150"},{"key":"3358_CR8","unstructured":"Krizhevsky, A., Hinton, G.: Convolutional deep belief networks on cifar-10. Unpublished manuscript 40(7), 1\u20139 (2010)"},{"key":"3358_CR9","doi-asserted-by":"crossref","unstructured":"Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.v.d.: Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1705\u20131714 (2019)","DOI":"10.1109\/ICCV.2019.00179"},{"issue":"23","key":"3358_CR10","doi-asserted-by":"publisher","first-page":"6469","DOI":"10.1016\/j.ijleo.2013.05.004","volume":"124","author":"JL Raheja","year":"2013","unstructured":"Raheja, J.L., Kumar, S., Chaudhary, A.: Fabric defect detection based on GLCM and Gabor filter: a comparison. Optik 124(23), 6469\u20136474 (2013)","journal-title":"Optik"},{"issue":"2","key":"3358_CR11","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/28.993164","volume":"38","author":"A Kumar","year":"2002","unstructured":"Kumar, A., Pang, G.K.: Defect detection in textured materials using Gabor filters. IEEE Trans. Ind. Appl. 38(2), 425\u2013440 (2002)","journal-title":"IEEE Trans. Ind. Appl."},{"issue":"1","key":"3358_CR12","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1080\/00405000.2012.692940","volume":"104","author":"J Jing","year":"2013","unstructured":"Jing, J., Zhang, H., Wang, J., Li, P., Jia, J.: Fabric defect detection using Gabor filters and defect classification based on LBP and Tamura method. J. Text. Inst. 104(1), 18\u201327 (2013)","journal-title":"J. Text. Inst."},{"issue":"10","key":"3358_CR13","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1016\/j.imavis.2009.03.007","volume":"27","author":"K-L Mak","year":"2009","unstructured":"Mak, K.-L., Peng, P., Yiu, K.F.C.: Fabric defect detection using morphological filters. Image Vis. Comput. 27(10), 1585\u20131592 (2009)","journal-title":"Image Vis. Comput."},{"issue":"6","key":"3358_CR14","doi-asserted-by":"publisher","first-page":"2132","DOI":"10.1016\/j.patcog.2009.12.001","volume":"43","author":"HY Ngan","year":"2010","unstructured":"Ngan, H.Y., Pang, G.K., Yung, N.H.: Ellipsoidal decision regions for motif-based patterned fabric defect detection. Pattern Recognit. 43(6), 2132\u20132144 (2010)","journal-title":"Pattern Recognit."},{"key":"3358_CR15","doi-asserted-by":"crossref","unstructured":"Zhou, J., Semenovich, D., Sowmya, A., Wang, J.: Sparse dictionary reconstruction for textile defect detection. In: International Conference on Machine Learning and Applications, vol. 1, pp. 21\u201326. IEEE (2012)","DOI":"10.1109\/ICMLA.2012.13"},{"key":"3358_CR16","doi-asserted-by":"publisher","first-page":"18042","DOI":"10.1109\/ACCESS.2019.2896078","volume":"7","author":"RA Lizarraga-Morales","year":"2019","unstructured":"Lizarraga-Morales, R.A., Correa-Tome, F.E., Sanchez-Yanez, R.E., Cepeda-Negrete, J.: On the use of binary features in a rule-based approach for defect detection on patterned textiles. IEEE Access 7, 18042\u201318049 (2019)","journal-title":"IEEE Access"},{"key":"3358_CR17","doi-asserted-by":"publisher","first-page":"221808","DOI":"10.1109\/ACCESS.2020.3041849","volume":"8","author":"X Kang","year":"2020","unstructured":"Kang, X., Zhang, E.: A universal and adaptive fabric defect detection algorithm based on sparse dictionary learning. IEEE Access 8, 221808\u2013221830 (2020)","journal-title":"IEEE Access"},{"key":"3358_CR18","doi-asserted-by":"crossref","unstructured":"Wei, B., Hao, K., Tang, X.-s., Ren, L.: Fabric defect detection based on faster RCNN. In: Proceedings of the Artificial Intelligence on Fashion and Textiles, pp. 45\u201351. Springer (2018)","DOI":"10.1007\/978-3-319-99695-0_6"},{"key":"3358_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Cui, J., Li, C., Wei, M., Yang, Y.: Fabric defect detection based on lightweight neural network. In: Chinese Conference on Pattern Recognition and Computer Vision, pp. 528\u2013539. Springer (2019)","DOI":"10.1007\/978-3-030-31654-9_45"},{"key":"3358_CR20","unstructured":"Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"issue":"3","key":"3358_CR21","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1111\/cote.12394","volume":"135","author":"J-F Jing","year":"2019","unstructured":"Jing, J.-F., Ma, H., Zhang, H.-H.: Automatic fabric defect detection using a deep convolutional neural network. Color. Technol. 135(3), 213\u2013223 (2019)","journal-title":"Color. Technol."},{"issue":"4","key":"3358_CR22","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.3390\/s18041064","volume":"18","author":"S Mei","year":"2018","unstructured":"Mei, S., Wang, Y., Wen, G.: Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model. Sensors 18(4), 1064 (2018)","journal-title":"Sensors"},{"key":"3358_CR23","doi-asserted-by":"publisher","first-page":"182320","DOI":"10.1109\/ACCESS.2019.2959880","volume":"7","author":"H Xie","year":"2019","unstructured":"Xie, H., Zhang, Y., Wu, Z.: Fabric defect detection method combing image pyramid and direction template. IEEE Access 7, 182320\u2013182334 (2019)","journal-title":"IEEE Access"},{"key":"3358_CR24","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27, pp. 2672\u20132680 (2014)"},{"issue":"3\u20134","key":"3358_CR25","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1177\/0040517519862880","volume":"90","author":"G Hu","year":"2020","unstructured":"Hu, G., Huang, J., Wang, Q., Li, J., Xu, Z., Huang, X.: Unsupervised fabric defect detection based on a deep convolutional generative adversarial network. Text. Res. J. 90(3\u20134), 247\u2013270 (2020)","journal-title":"Text. Res. J."},{"key":"3358_CR26","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman\u00a0Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3\u201311. Springer (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"3358_CR27","unstructured":"Weston, J., Chopra, S., Bordes, A.: Memory networks. arXiv preprint arXiv:1410.3916 (2014)"},{"key":"3358_CR28","unstructured":"Rae, J.W., Hunt, J.J., Harley, T., Danihelka, I., Senior, A., Wayne, G., Graves, A., Lillicrap, T.P.: Scaling memory-augmented neural networks with sparse reads and writes. arXiv preprint arXiv:1610.09027 (2016)"},{"key":"3358_CR29","unstructured":"Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: Artificial Intelligence and Statistics, pp. 562\u2013570. PMLR (2015)"},{"key":"3358_CR30","doi-asserted-by":"crossref","unstructured":"Reiss, T., Cohen, N., Bergman, L., Hoshen, Y.: Panda: Adapting pretrained features for anomaly detection and segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2806\u20132814 (2021)","DOI":"10.1109\/CVPR46437.2021.00283"},{"key":"3358_CR31","unstructured":"Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S.A., Binder, A., M\u00fcller, E., Kloft, M.: Deep one-class classification. In: International Conference on Machine Learning, pp. 4393\u20134402. PMLR (2018)"},{"issue":"3","key":"3358_CR32","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1515\/aut-2015-0001","volume":"15","author":"D Zhu","year":"2015","unstructured":"Zhu, D., Pan, R., Gao, W., Zhang, J.: Yarn-dyed fabric defect detection based on autocorrelation function and GLCM. Autex Res. J. 15(3), 226\u2013232 (2015)","journal-title":"Autex Res. J."},{"key":"3358_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, H., Tang, W., Zhang, L., Li, P., Gu, D.: Defect detection of yarn-dyed shirts based on denoising convolutional self-encoder. In: IEEE Data Driven Control and Learning Systems Conference, pp. 1263\u20131268. IEEE (2019)","DOI":"10.1109\/DDCLS.2019.8908944"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03358-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03358-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03358-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T22:56:54Z","timestamp":1737759414000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03358-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,14]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["3358"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03358-7","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2024,4,14]]},"assertion":[{"value":"1 March 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 April 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}