{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:18:43Z","timestamp":1780053523318,"version":"3.54.0"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031928048","type":"print"},{"value":"9783031928055","type":"electronic"}],"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\/978-3-031-92805-5_2","type":"book-chapter","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T12:59:10Z","timestamp":1747918750000},"page":"18-34","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Neurosymbolic Visual Transform Based on\u00a0Logic Tensor Network for\u00a0Defect Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0135-7450","authenticated-orcid":false,"given":"Youcef","family":"Djenouri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9233-3723","authenticated-orcid":false,"given":"Ahmed Nabil","family":"Belbachir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7103-2179","authenticated-orcid":false,"given":"Asma","family":"Belhadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5288-0324","authenticated-orcid":false,"given":"Tomasz","family":"Michalak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"2_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/978-3-030-20893-6_39","volume-title":"Computer Vision \u2013 ACCV 2018","author":"S Akcay","year":"2019","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622\u2013637. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20893-6_39"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Artola, A., Kolodziej, Y., Morel, J.M., Ehret, T.: GLAD: a global-to-local anomaly detector. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 5501\u20135510 (2023)","DOI":"10.1109\/WACV56688.2023.00546"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Badreddine, S., Garcez, A.D., Serafini, L., Spranger, M.: Logic tensor networks. Artif. Intell. 303, 103649 (2022)","DOI":"10.1016\/j.artint.2021.103649"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: MVTec AD\u2013a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9592\u20139600 (2019)","DOI":"10.1109\/CVPR.2019.00982"},{"key":"2_CR5","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.neunet.2021.12.008","volume":"147","author":"L Chen","year":"2022","unstructured":"Chen, L., You, Z., Zhang, N., Xi, J., Le, X.: UTRAD: anomaly detection and localization with U-transformer. Neural Netw. 147, 53\u201362 (2022). https:\/\/doi.org\/10.1016\/j.neunet.2021.12.008","journal-title":"Neural Netw."},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Cho, W., Park, J., Choo, J.: Training auxiliary prototypical classifiers for explainable anomaly detection in medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2624\u20132633 (2023)","DOI":"10.1109\/WACV56688.2023.00265"},{"key":"2_CR7","doi-asserted-by":"publisher","unstructured":"Djenouri, Y., Belbachir, A.N., Jhaveri, R.H., Djenouri, D.: Knowledge guided deep learning for general-purpose computer vision applications. In: Tsapatsoulis, N., et al. (eds.) CAIP 2023. LNCS, vol. 14184, pp. 185\u2013194. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-44237-7_18","DOI":"10.1007\/978-3-031-44237-7_18"},{"issue":"4","key":"2_CR8","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1109\/MIM.2024.10540405","volume":"27","author":"Y Djenouri","year":"2024","unstructured":"Djenouri, Y., Srivastava, G., Lin, J.: Applied AI in defect detection for additive manufacturing: current literature, metrics, datasets, and open challenges. IEEE Instrum. Measur. Mag. 27(4), 46\u201353 (2024)","journal-title":"IEEE Instrum. Measur. Mag."},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Donadello, I., Serafini, L., Garcez, A.D.: Logic tensor networks for semantic image interpretation. arXiv preprint arXiv:1705.08968 (2017)","DOI":"10.24963\/ijcai.2017\/221"},{"key":"2_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2022.103689","volume":"140","author":"L Gao","year":"2022","unstructured":"Gao, L., Zhang, J., Yang, C., Zhou, Y.: Cas-VSwin transformer: a variant Swin transformer for surface-defect detection. Comput. Ind. 140, 103689 (2022)","journal-title":"Comput. Ind."},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Garcez, A.D., Lamb, L.C.: Neurosymbolic AI: the 3rd wave. Artif. Intell. Rev. 56(11), 12387\u201312406 (2023)","DOI":"10.1007\/s10462-023-10448-w"},{"key":"2_CR12","doi-asserted-by":"publisher","unstructured":"Goyal, A., Mandal, M., Hassija, V., Aloqaily, M., Chamola, V.: Captionomaly: a deep learning toolbox for anomaly captioning in social surveillance systems. IEEE Trans. Comput. Soc. Syst. (2023). https:\/\/doi.org\/10.1109\/TCSS.2022.3230262","DOI":"10.1109\/TCSS.2022.3230262"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Gudovskiy, D., Ishizaka, S., Kozuka, K.: CFLOW-AD: real-time unsupervised anomaly detection with localization via conditional normalizing flows. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 98\u2013107 (2022)","DOI":"10.1109\/WACV51458.2022.00188"},{"key":"2_CR14","doi-asserted-by":"publisher","unstructured":"Huang, C., et al.: Weakly supervised video anomaly detection via self-guided temporal discriminative transformer. IEEE Trans. Cybern. (2022). https:\/\/doi.org\/10.1109\/TCYB.2022.3227044","DOI":"10.1109\/TCYB.2022.3227044"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Huang, C., et al.: Pixel-level anomaly detection via uncertainty-aware prototypical transformer. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 521\u2013530 (2022)","DOI":"10.1145\/3503161.3548082"},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"Huang, C., et al.: Self-supervised attentive generative adversarial networks for video anomaly detection. IEEE Trans. Neural Netw. Learn. Syst. (2022). https:\/\/doi.org\/10.1109\/TNNLS.2022.3159538","DOI":"10.1109\/TNNLS.2022.3159538"},{"key":"2_CR17","doi-asserted-by":"publisher","unstructured":"Jeon, M., Yoo, S., Kim, S.W.: A contactless pcba defect detection method: Convolutional neural networks with thermographic images. IEEE Trans. Compon. Packag. Manuf. Technol. (2022). https:\/\/doi.org\/10.1109\/TCPMT.2022.3147319","DOI":"10.1109\/TCPMT.2022.3147319"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Lee, X.Y., et al.: XDNet: a few-shot meta-learning approach for cross-domain visual inspection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4374\u20134383 (2023)","DOI":"10.1109\/CVPRW59228.2023.00460"},{"issue":"1","key":"2_CR19","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/JAS.2022.105935","volume":"10","author":"X Li","year":"2022","unstructured":"Li, X., Xu, Y., Li, N., Yang, B., Lei, Y.: Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks. IEEE\/CAA J. Automatica Sinica 10(1), 121\u2013134 (2022). https:\/\/doi.org\/10.1109\/JAS.2022.105935","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"2_CR20","doi-asserted-by":"publisher","unstructured":"Li, X., Yu, S., Lei, Y., Li, N., Yang, B.: Intelligent machinery fault diagnosis with event-based camera. IEEE Trans. Industr. Inf. (2023). https:\/\/doi.org\/10.1109\/TII.2023.3262854","DOI":"10.1109\/TII.2023.3262854"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Lima, L., Andrade, F., Djenouri, Y., Pfeiffer, C., Moura, M.: Empowering search and rescue operations with big data technology: a comprehensive study of YOLOv8 transfer learning for transportation safety. In: 2023 IEEE International Conference on Big Data (BigData), pp. 2616\u20132623. IEEE (2023)","DOI":"10.1109\/BigData59044.2023.10386965"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Liu, H., Chen, C., Hu, R., Bin, J., Dong, H., Liu, Z.: CGTD-Net: channel-wise global transformer based dual-branch network for industrial strip steel surface defect detection. IEEE Sens. J. (2024)","DOI":"10.1109\/JSEN.2023.3346470"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1975\u20131981. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Maji, S., Berg, A.C., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.\u00a01\u20138. IEEE (2008)","DOI":"10.1109\/CVPR.2008.4587630"},{"issue":"10","key":"2_CR25","doi-asserted-by":"publisher","first-page":"3126","DOI":"10.1109\/TUFFC.2021.3081750","volume":"68","author":"D Medak","year":"2021","unstructured":"Medak, D., Posilovi\u0107, L., Suba\u0161i\u0107, M., Budimir, M., Lon\u010dari\u0107, S.: Automated defect detection from ultrasonic images using deep learning. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 68(10), 3126\u20133134 (2021). https:\/\/doi.org\/10.1109\/TUFFC.2021.3081750","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"issue":"3","key":"2_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-021-01195-5","volume":"32","author":"D Mery","year":"2021","unstructured":"Mery, D.: Aluminum casting inspection using deep object detection methods and simulated ellipsoidal defects. Mach. Vis. Appl. 32(3), 1\u201316 (2021). https:\/\/doi.org\/10.1007\/s00138-021-01195-5","journal-title":"Mach. Vis. Appl."},{"issue":"10","key":"2_CR27","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1145\/3554918","volume":"65","author":"D Monroe","year":"2022","unstructured":"Monroe, D.: Neurosymbolic AI. Commun. ACM 65(10), 11\u201313 (2022)","journal-title":"Commun. ACM"},{"key":"2_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105636","volume":"118","author":"IE Parlak","year":"2023","unstructured":"Parlak, I.E., Emel, E.: Deep learning-based detection of aluminum casting defects and their types. Eng. Appl. Artif. Intell. 118, 105636 (2023). https:\/\/doi.org\/10.1016\/j.engappai.2022.105636","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"9","key":"2_CR29","doi-asserted-by":"publisher","first-page":"5787","DOI":"10.1002\/mp.15852","volume":"49","author":"X Qu","year":"2022","unstructured":"Qu, X., et al.: A VGG attention vision transformer network for benign and malignant classification of breast ultrasound images. Med. Phys. 49(9), 5787\u20135798 (2022). https:\/\/doi.org\/10.1002\/mp.15852","journal-title":"Med. Phys."},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Ren, G., Lazarou, M., Yuan, J., Stathaki, T.: Towards automated polyp segmentation using weakly-and semi-supervised learning and deformable transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4354\u20134363 (2023)","DOI":"10.1109\/CVPRW59228.2023.00458"},{"key":"2_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105732","volume":"119","author":"EK Sahin","year":"2023","unstructured":"Sahin, E.K., Demir, S.: Greedy-AutoML: a novel greedy-based stacking ensemble learning framework for assessing soil liquefaction potential. Eng. Appl. Artif. Intell. 119, 105732 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"6","key":"2_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3417989","volume":"53","author":"KK Santhosh","year":"2020","unstructured":"Santhosh, K.K., Dogra, D.P., Roy, P.P.: Anomaly detection in road traffic using visual surveillance: a survey. ACM Comput. Surv. (CSUR) 53(6), 1\u201326 (2020). https:\/\/doi.org\/10.1145\/3417989","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"2_CR33","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1007\/978-3-319-49130-1_25","volume-title":"AI*IA 2016 Advances in Artificial Intelligence","author":"L Serafini","year":"2016","unstructured":"Serafini, L., d\u2019Avila Garcez, A.S.: Learning and reasoning with logic tensor networks. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds.) AI*IA 2016. LNCS (LNAI), vol. 10037, pp. 334\u2013348. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49130-1_25"},{"key":"2_CR34","doi-asserted-by":"publisher","unstructured":"Singh, S.A., Desai, K.A.: Automated surface defect detection framework using machine vision and convolutional neural networks. J. Intell. Manuf., 1\u201317 (2021). https:\/\/doi.org\/10.1007\/s10845-021-01878-w","DOI":"10.1007\/s10845-021-01878-w"},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: transformer for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7262\u20137272 (2021)","DOI":"10.1109\/ICCV48922.2021.00717"},{"key":"2_CR36","doi-asserted-by":"crossref","unstructured":"Sultani, W., Chen, C., Shah, M.: Real-world anomaly detection in surveillance videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6479\u20136488 (2018)","DOI":"10.1109\/CVPR.2018.00678"},{"key":"2_CR37","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.neunet.2022.01.012","volume":"148","author":"T Sun","year":"2022","unstructured":"Sun, T., Ding, S., Guo, L.: Low-degree term first in ResNet, its variants and the whole neural network family. Neural Netw. 148, 155\u2013165 (2022). https:\/\/doi.org\/10.1016\/j.neunet.2022.01.012","journal-title":"Neural Netw."},{"key":"2_CR38","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q.V.: EfficientDet: scalable and efficient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781\u201310790 (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"2_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109456","volume":"253","author":"W Ullah","year":"2022","unstructured":"Ullah, W., Hussain, T., Khan, Z.A., Haroon, U., Baik, S.W.: Intelligent dual stream CNN and echo state network for anomaly detection. Knowl.-Based Syst. 253, 109456 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.109456","journal-title":"Knowl.-Based Syst."},{"key":"2_CR41","doi-asserted-by":"crossref","unstructured":"Varga, L.A., Kiefer, B., Messmer, M., Zell, A.: SeaDronesSee: a maritime benchmark for detecting humans in open water. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2260\u20132270 (2022)","DOI":"10.1109\/WACV51458.2022.00374"},{"key":"2_CR42","doi-asserted-by":"crossref","unstructured":"Wu, J.C., Chen, D.J., Fuh, C.S., Liu, T.L.: Learning unsupervised Metaformer for anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4369\u20134378 (2021)","DOI":"10.1109\/ICCV48922.2021.00433"},{"key":"2_CR43","unstructured":"Yang, M.: Visual transformer for object detection. arXiv preprint arXiv:2206.06323 (2022)"},{"key":"2_CR44","doi-asserted-by":"crossref","unstructured":"Zavrtanik, V., Kristan, M., Sko\u010daj, D.: DRAEM-a discriminatively trained reconstruction embedding for surface anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8330\u20138339 (2021)","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"2_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107706","volume":"112","author":"V Zavrtanik","year":"2021","unstructured":"Zavrtanik, V., Kristan, M., Sko\u010daj, D.: Reconstruction by inpainting for visual anomaly detection. Pattern Recogn. 112, 107706 (2021). https:\/\/doi.org\/10.1016\/j.patcog.2020.107706","journal-title":"Pattern Recogn."},{"key":"2_CR46","doi-asserted-by":"crossref","unstructured":"Zhou, H., Yang, R., Hu, R., Shu, C., Tang, X., Li, X.: ETDNet: efficient transformer-based detection network for surface defect detection. IEEE Trans. Instrum. Measur. (2023)","DOI":"10.1109\/TIM.2023.3307753"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-92805-5_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T12:59:27Z","timestamp":1747918767000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-92805-5_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031928048","9783031928055"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-92805-5_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}