{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:47:19Z","timestamp":1743032839847,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031773884"},{"type":"electronic","value":"9783031773891"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-77389-1_30","type":"book-chapter","created":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T18:32:19Z","timestamp":1737484339000},"page":"383-395","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Image-Based Method for Defect Detection on Metal Surfaces"],"prefix":"10.1007","author":[{"given":"Sida","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Richard J.","family":"Povinelli","sequence":"additional","affiliation":[]},{"given":"Joseph","family":"Domblesky","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,22]]},"reference":[{"key":"30_CR1","doi-asserted-by":"publisher","unstructured":"Bhatt, P.M., Malhan, R.K., Rajendran, P., Shah, B.C., Thakar, S., Yoon, Y.J., Gupta, S.K.: Image-based surface defect detection using deep learning: a review. ASME.\u00a0J. Comput. Inf. Sci. Eng. 21(4), 040801 (2021).\u00a0https:\/\/doi.org\/10.1115\/1.4049535","DOI":"10.1115\/1.4049535"},{"key":"30_CR2","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1007\/s001700070055","volume":"16","author":"DM Tsa","year":"2000","unstructured":"Tsa, D.M., Wu, S.K.: Automated surface inspection using gabor filters. Int. J. Adv. Manuf. Technol. 16, 474\u2013482 (2000). https:\/\/doi.org\/10.1007\/s001700070055","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"30_CR3","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1016\/j.neucom.2020.06.090","volume":"412","author":"T Wei","year":"2020","unstructured":"Wei, T., Cao, D., Zheng, C., Yang, Q.: A simulation-based few samples learning method for surface defect segmentation. Neurocomputing 412, 461\u2013476 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2020.06.090","journal-title":"Neurocomputing"},{"issue":"4","key":"30_CR4","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/s0262-8856(03)00007-6","volume":"21","author":"D-M Tsai","year":"2003","unstructured":"Tsai, D.-M., Huang, T.-Y.: Automated surface inspection for statistical textures. Image Vis. Comput. 21(4), 307\u2013323 (2003). https:\/\/doi.org\/10.1016\/s0262-8856(03)00007-6","journal-title":"Image Vis. Comput."},{"key":"30_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2021.103551","volume":"134","author":"M Chen","year":"2022","unstructured":"Chen, M., et al.: Improved faster R-CNN for fabric defect detection based on Gabor filter with genetic algorithm optimization. Comput. Ind. 134, 103551 (2022). https:\/\/doi.org\/10.1016\/j.compind.2021.103551","journal-title":"Comput. Ind."},{"key":"30_CR6","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.neucom.2019.10.067","volume":"380","author":"Y Zhao","year":"2020","unstructured":"Zhao, Y., Hao, K., He, H., Tang, X., Wei, B.: A visual long-short-term memory based integrated CNN model for fabric defect image classification. Neurocomputing 380, 259\u2013270 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2019.10.067","journal-title":"Neurocomputing"},{"key":"30_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.polymertesting.2020.106585","volume":"89","author":"AJ Hansen","year":"2020","unstructured":"Hansen, A.J., Knoche, H., Moeslund, T.B.: Fantastic plastic? an image-based test method to detect aesthetic defects in batches based on reference samples. Polym. Testing 89, 106585 (2020). https:\/\/doi.org\/10.1016\/j.polymertesting.2020.106585","journal-title":"Polym. Testing"},{"key":"30_CR8","doi-asserted-by":"publisher","unstructured":"Wong, W.K., Jiang, J.L.: Computer vision techniques for detecting fabric defects. Applications of Computer Vision in Fashion and Textiles, 47\u201360 (2018). https:\/\/doi.org\/10.1016\/b978-0-08-101217-8.00003-8","DOI":"10.1016\/b978-0-08-101217-8.00003-8"},{"key":"30_CR9","doi-asserted-by":"publisher","unstructured":"Lin, C.-H., Ho, C.-W., Hu, G.-H., Kuo, P.-C., Hu, C.-Y.: Alloy cast product defect detection based on object detection. In: 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Hualien City, Taiwan, pp. 1\u20132 (2021). https:\/\/doi.org\/10.1109\/ISPACS51563.2021.9651119","DOI":"10.1109\/ISPACS51563.2021.9651119"},{"key":"30_CR10","unstructured":"Sun, I.-S., Jeong, H.: Defect detection of aerial images without reference image. In: 2012 12th International Conference on Control, Automation and Systems, Jeju, Korea (South), pp. 1275\u20131278 (2012)"},{"key":"30_CR11","doi-asserted-by":"publisher","unstructured":"Lv, S., Zhou, F., Wei, Z.: Train wheel tread defects detection based on image registration. In: 2017 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, China, pp. 1\u20134 (2017). https:\/\/doi.org\/10.1109\/IST.2017.8261509","DOI":"10.1109\/IST.2017.8261509"},{"issue":"5","key":"30_CR12","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1109\/TPAMI.2004.1273918","volume":"26","author":"DR Martin","year":"2004","unstructured":"Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530\u2013549 (2004). https:\/\/doi.org\/10.1109\/TPAMI.2004.1273918","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"30_CR13","first-page":"448","volume":"28","author":"Z Zhao","year":"2019","unstructured":"Zhao, Z., Zheng, P., Xu, S., Wu, X.: Dense extreme inception network: Towards a robust CNN model for edge detection. IEEE Trans. Image Process. 28(1), 448\u2013461 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"30_CR14","doi-asserted-by":"publisher","unstructured":"Sun, F., Luo, Z., Li, S.: Boundary difference over\u00a0union loss for\u00a0medical image segmentation. In: Greenspan, H.,\u00a0et al.\u00a0Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol. 14223. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43901-8_28","DOI":"10.1007\/978-3-031-43901-8_28"}],"container-title":["Lecture Notes in Computer Science","Advances in Visual Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-77389-1_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T18:32:21Z","timestamp":1737484341000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-77389-1_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031773884","9783031773891"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-77389-1_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"22 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISVC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Visual Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lake Tahoe, NV","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isvc2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isvc.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}