{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:59:05Z","timestamp":1743065945987,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030688202"},{"type":"electronic","value":"9783030688219"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-68821-9_5","type":"book-chapter","created":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T20:03:56Z","timestamp":1613851436000},"page":"51-62","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Tire Surface Segmentation in Infrared Imaging with Convolutional Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0314-7898","authenticated-orcid":false,"given":"Rodrigo","family":"Nava","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duc","family":"Fehr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frank","family":"Petry","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2363-6055","authenticated-orcid":false,"given":"Thomas","family":"Tamisier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,21]]},"reference":[{"key":"5_CR1","series-title":"Proceedings","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1007\/978-3-658-08844-6_62","volume-title":"15. Internationales Stuttgarter Symposium","author":"F B\u00fcttner","year":"2015","unstructured":"B\u00fcttner, F., Unterreiner, M., Bortolussi, P.: An effective method to identify thermodynamic tire characteristics through driving maneuvers. In: Bargende, M., Reuss, H.-C., Wiedemann, J. (eds.) 15. Internationales Stuttgarter Symposium. P, pp. 921\u2013936. Springer, Wiesbaden (2015). https:\/\/doi.org\/10.1007\/978-3-658-08844-6_62"},{"key":"5_CR2","series-title":"Proceedings","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/978-3-658-09711-0_48","volume-title":"6th International Munich Chassis Symposium 2015","author":"F Calabrese","year":"2015","unstructured":"Calabrese, F., B\u00e4cker, M., Gallrein, A.: Evaluation of different modeling approaches for the tire handling simulations \u2013 analysis and results. In: Pfeffer, P. (ed.) 6th International Munich Chassis Symposium 2015. P, pp. 749\u2013773. Springer, Wiesbaden (2015). https:\/\/doi.org\/10.1007\/978-3-658-09711-0_48"},{"key":"5_CR3","doi-asserted-by":"publisher","unstructured":"Chan, L., Hosseini, M.S., Plataniotis, K.N.: A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains. Int. J. Comput. Vis. 1\u201324 (2020). https:\/\/doi.org\/10.1007\/s11263-020-01373-4","DOI":"10.1007\/s11263-020-01373-4"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., Culurciello, E.: Linknet: exploiting encoder representations for efficient semantic segmentation. In: IEEE Visual Communications and Image Processing (VCIP), pp. 1\u20134 (2017)","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"5_CR5","unstructured":"Corollaro, A.: Essentiality of temperature management while modeling and analyzing tires contact forces. Ph.D. thesis, Universit\u00e1 degli studi di Napoli Federico II (2014)"},{"key":"5_CR6","unstructured":"Dehghani, M., Severyn, A., Rothe, S., Kamps, J.: Avoiding your teacher\u2019s mistakes: training neural networks with controlled weak supervision. arXiv preprint arXiv:1711.00313 (2017)"},{"key":"5_CR7","doi-asserted-by":"publisher","first-page":"1560","DOI":"10.1016\/j.protcy.2014.10.178","volume":"16","author":"A Duarte","year":"2014","unstructured":"Duarte, A., et al.: Segmentation algorithms for thermal images. Procedia Technology 16, 1560\u20131569 (2014)","journal-title":"Procedia Technology"},{"key":"5_CR8","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121\u20132159 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR9","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/s11012-013-9821-9","volume":"49","author":"F Farroni","year":"2014","unstructured":"Farroni, F., Giordano, D., Russo, M., Timpone, F.: TRT: thermo racing tyre a physical model to predict the tyre temperature distribution. Meccanica 49, 707\u2013723 (2014)","journal-title":"Meccanica"},{"key":"5_CR10","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.asoc.2018.05.018","volume":"70","author":"A Garcia-Garcia","year":"2018","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Martinez-Gonzalez, P., Garcia-Rodriguez, J.: A survey on deep learning techniques for image and video semantic segmentation. Appl. Soft Comput. 70, 41\u201365 (2018)","journal-title":"Appl. Soft Comput."},{"key":"5_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/5092064","volume":"2018","author":"J Gauci","year":"2018","unstructured":"Gauci, J., et al.: Automated region extraction from thermal images for peripheral vascular disease monitoring. J. Healthc. Eng. 2018, 1\u201314 (2018)","journal-title":"J. Healthc. Eng."},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Gil, G., Park, J.: Physical handling tire model incorporating temperature and inflation pressure change effect. In: SAE Technical Paper. SAE International (2018)","DOI":"10.4271\/2018-01-1338"},{"issue":"24","key":"5_CR13","doi-asserted-by":"publisher","first-page":"5328","DOI":"10.3390\/app9245328","volume":"9","author":"D Harsh","year":"2019","unstructured":"Harsh, D., Shyrokau, B.: Tire model with temperature effects for formula SAE vehicle. Appl. Sci. 9(24), 5328 (2019)","journal-title":"Appl. Sci."},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR15","unstructured":"Iqbal, H.: Harisiqbal88\/plotneuralnet v1.0.0 (2018). https:\/\/doi.org\/10.5281\/zenodo.2526396"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Iva\u0161i\u0107-Kos, M., Kri\u0161to, M., Pobar, M.: Human detection in thermal imaging using YOLO. In: Proceedings of the 2019 5th International Conference on Computer and Technology Applications. ICCTA 2019, Association for Computing Machinery, pp. 20\u201324 (2019)","DOI":"10.1145\/3323933.3324076"},{"key":"5_CR17","unstructured":"Jangblad, M.: Object detection in infrared images using deep convolutional neural networks. Master\u2019s thesis, Uppsala University (2018)"},{"issue":"2","key":"5_CR18","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T Lin","year":"2020","unstructured":"Lin, T., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318\u2013327 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"5_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"5_CR22","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Singh, K., Sarvari, P., Petry, F., Khadraoui, D.: Application of machine learning & deep learning techniques in the context of use cases relevant for the tire industry. In: VDI Wissensforum, pp. 1\u201324 (2019\u201310)","DOI":"10.51202\/9783181023563-241"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"5_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++:a nested U-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition. ICPR International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68821-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T20:05:02Z","timestamp":1613851502000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68821-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030688202","9783030688219"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68821-9_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ICPR2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icpr2020.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}