{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:49:14Z","timestamp":1742935754001,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030898168"},{"type":"electronic","value":"9783030898175"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/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":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-89817-5_12","type":"book-chapter","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T20:35:26Z","timestamp":1634762126000},"page":"161-172","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Comparing Machine Learning Based Segmentation Models on Jet Fire Radiation Zones"],"prefix":"10.1007","author":[{"given":"Carmina","family":"P\u00e9rez-Guerrero","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adriana","family":"Palacios","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gilberto","family":"Ochoa-Ruiz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Mata","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miguel","family":"Gonzalez-Mendoza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luis Eduardo","family":"Falc\u00f3n-Morales","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","first-page":"182381","DOI":"10.1109\/ACCESS.2019.2960209","volume":"7","author":"M Ajith","year":"2019","unstructured":"Ajith, M., Mart\u00ednez-Ram\u00f3n, M.: Unsupervised segmentation of fire and smoke from infra-red videos. IEEE Access 7, 182381\u2013182394 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2960209","journal-title":"IEEE Access"},{"issue":"12","key":"12_CR2","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"12_CR3","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/83.902291","volume":"10","author":"T Chan","year":"2001","unstructured":"Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266\u2013277 (2001). https:\/\/doi.org\/10.1109\/83.902291","journal-title":"IEEE Trans. Image Process."},{"key":"12_CR4","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation (2017). arXiv: 1706.05587"},{"key":"12_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/978-3-319-75238-9_6","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"L Fidon","year":"2018","unstructured":"Fidon, L., et al.: Generalised Wasserstein dice score for imbalanced multi-class segmentation using\u00a0holistic convolutional networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 64\u201376. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75238-9_6"},{"key":"12_CR6","doi-asserted-by":"publisher","unstructured":"Foroughi, V., et al.: Thermal effects of a sonic jet fire impingement on a pipe. J. Loss Prev. Process Ind. 71, 104449 (2021). https:\/\/doi.org\/10.1016\/j.jlp.2021.104449","DOI":"10.1016\/j.jlp.2021.104449"},{"key":"12_CR7","doi-asserted-by":"publisher","unstructured":"Janssen, R., Sepasian, N.: Automatic flare-stack monitoring. SPE Prod. Oper. 34(01), 18\u201323 (2018). https:\/\/doi.org\/10.2118\/187257-PA","DOI":"10.2118\/187257-PA"},{"issue":"02","key":"12_CR8","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., Dollar, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(02), 318\u2013327 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2018.2858826","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"12_CR9","doi-asserted-by":"publisher","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017). https:\/\/doi.org\/10.1016\/j.media.2017.07.005","DOI":"10.1016\/j.media.2017.07.005"},{"key":"12_CR10","unstructured":"Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas (2018). arXiv: 1804.03999"},{"key":"12_CR11","unstructured":"Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: ENet: a deep neural network architecture for real-time semantic segmentation (2016). arXiv: 1606.02147"},{"key":"12_CR12","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 8024\u20138035. Curran Associates, Inc. (2019)"},{"key":"12_CR13","doi-asserted-by":"publisher","unstructured":"Rodrigues, S.J., Yan, Y.: Application of digital imaging techniques to flare monitoring. J. Phys. Conf. Ser. 307, 012048 (2011). https:\/\/doi.org\/10.1088\/1742-6596\/307\/1\/012048","DOI":"10.1088\/1742-6596\/307\/1\/012048"},{"key":"12_CR14","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":"12_CR15","doi-asserted-by":"publisher","unstructured":"Rudz, S., Chetehouna, K., Hafiane, A., Laurent, H., S\u00e9ro-Guillaume, O.: Investigation of a novel image segmentation method dedicated to forest fire applications*. Meas. Sci. Technol. 24, 075403 (2013). https:\/\/doi.org\/10.1088\/0957-0233\/24\/7\/075403","DOI":"10.1088\/0957-0233\/24\/7\/075403"},{"key":"12_CR16","doi-asserted-by":"publisher","unstructured":"Taha, A.A., Hanbury, A.: Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool. BMC Med. Imag. 15(29) (2015). https:\/\/doi.org\/10.1186\/s12880-015-0068-x","DOI":"10.1186\/s12880-015-0068-x"},{"key":"12_CR17","unstructured":"Yuheng, S., Hao, Y.: Image segmentation algorithms overview (2017). arXiv:1707.02051"},{"key":"12_CR18","doi-asserted-by":"publisher","unstructured":"Zaitoun, N., Aqel, M.: Survey on image segmentation techniques. Procedia Comput. Sci. 65, 797\u2013806 (2015). https:\/\/doi.org\/10.1016\/j.procs.2015.09.027","DOI":"10.1016\/j.procs.2015.09.027"},{"key":"12_CR19","doi-asserted-by":"publisher","unstructured":"Zhang, R., Zhu, S., Zhou, Q.: A novel gradient vector flow snake model based on convex function for infrared image segmentation. Sensors 16, 1756 (2016). https:\/\/doi.org\/10.3390\/s16101756","DOI":"10.3390\/s16101756"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89817-5_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:02:51Z","timestamp":1710360171000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89817-5_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030898168","9783030898175"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89817-5_12","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 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican International Conference on Artificial Intelligence","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":"25 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micai.org\/2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"129","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}