{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T15:14:11Z","timestamp":1743002051818,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031591662"},{"type":"electronic","value":"9783031591679"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-59167-9_13","type":"book-chapter","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T07:02:44Z","timestamp":1714114964000},"page":"150-161","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence for\u00a0Automated Marine Growth Segmentation"],"prefix":"10.1007","author":[{"given":"Jo\u00e3o","family":"Carvalho","sequence":"first","affiliation":[]},{"given":"Pedro Nuno","family":"Leite","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Mina","sequence":"additional","affiliation":[]},{"given":"Louren\u00e7o","family":"Pinho","sequence":"additional","affiliation":[]},{"given":"Eduardo P.","family":"Gon\u00e7alves","sequence":"additional","affiliation":[]},{"given":"Andry Maykol","family":"Pinto","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,27]]},"reference":[{"issue":"2","key":"13_CR1","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.jnlssr.2022.02.001","volume":"3","author":"S Adumene","year":"2022","unstructured":"Adumene, S., Ikue-John, H.: Offshore system safety and operational challenges in harsh arctic operations. J. Safety Sci. Resilience 3(2), 153\u2013168 (2022). https:\/\/doi.org\/10.1016\/j.jnlssr.2022.02.001","journal-title":"J. Safety Sci. Resilience"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Apolinario, M., Coutinho, R.: Understanding the biofouling of offshore and deep-sea structures. In: Hellio, C., Yebra, D. (eds.) Advances in Marine Antifouling Coatings and Technologies, pp. 132\u2013147. Woodhead Publishing Series in Metals and Surface Engineering, Woodhead Publishing (2009). 10.1533\/9781845696313.1.132","DOI":"10.1533\/9781845696313.1.132"},{"issue":"12","key":"13_CR3","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."},{"key":"13_CR4","doi-asserted-by":"publisher","unstructured":"Campos, D., Matos, A., Pinto, A.: Multi-domain inspection of offshore wind farms using an autonomous surface vehicle. SN Appli. Sci. 3 (2021). https:\/\/doi.org\/10.1007\/s42452-021-04451-5","DOI":"10.1007\/s42452-021-04451-5"},{"issue":"3","key":"13_CR5","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1109\/83.661196","volume":"7","author":"F Chan","year":"1998","unstructured":"Chan, F., Lam, F., Zhu, H.: Adaptive thresholding by variational method. IEEE Trans. Image Process. 7(3), 468\u2013473 (1998). https:\/\/doi.org\/10.1109\/83.661196","journal-title":"IEEE Trans. Image Process."},{"key":"13_CR6","doi-asserted-by":"publisher","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv (2017). https:\/\/doi.org\/10.48550\/arXiv.1706.05587","DOI":"10.48550\/arXiv.1706.05587"},{"key":"13_CR7","doi-asserted-by":"publisher","unstructured":"Cheng, J., Xue, R., Lu, W., Jia, R.: Segmentation of medical images with canny operator and gvf snake model. In: 2008 7th World Congress on Intelligent Control and Automation. pp, 1777\u20131780 (2008). https:\/\/doi.org\/10.1109\/WCICA.2008.4593191","DOI":"10.1109\/WCICA.2008.4593191"},{"key":"13_CR8","doi-asserted-by":"publisher","unstructured":"Dion\u00edsio, J.M.M., Pereira, P.N.A.A.S., Leite, P.N., Neves, F.S., Tavares, J.M.R.S., Pinto, A.M.: Nereon - an underwater dataset for monocular depth estimation. In: OCEANS 2023 - Limerick. pp.\u00a01\u20137 (2023). https:\/\/doi.org\/10.1109\/OCEANSLimerick52467.2023.10244675","DOI":"10.1109\/OCEANSLimerick52467.2023.10244675"},{"key":"13_CR9","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/MCG.2016.26","volume":"36","author":"P Drews-Jr","year":"2016","unstructured":"Drews-Jr, P., Nascimento, E., Botelho, S., Campos, M.: Underwater depth estimation and image restoration based on single images. IEEE Comput. Graph. Appli. 36, 24\u201335 (2016). https:\/\/doi.org\/10.1109\/MCG.2016.26","journal-title":"IEEE Comput. Graph. Appli."},{"key":"13_CR10","doi-asserted-by":"publisher","unstructured":"Drews-Jr, P., Souza, I., Maurell, I., Protas, E., Botelho, S.: Underwater image segmentation in the wild using deep learning. J. Brazilian Comput. Soc. 27 (2021). https:\/\/doi.org\/10.1186\/s13173-021-00117-7","DOI":"10.1186\/s13173-021-00117-7"},{"key":"13_CR11","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"13_CR12","doi-asserted-by":"publisher","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv (2017). https:\/\/doi.org\/10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"key":"13_CR13","doi-asserted-by":"publisher","first-page":"76056","DOI":"10.1109\/ACCESS.2021.3082697","volume":"9","author":"PN Leite","year":"2021","unstructured":"Leite, P.N., Pinto, A.M.: Exploiting motion perception in depth estimation through a lightweight convolutional neural network. IEEE Access 9, 76056\u201376068 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3082697","journal-title":"IEEE Access"},{"key":"13_CR14","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4409685","author":"PN Leite","year":"2023","unstructured":"Leite, P.N., Pinto, A.M.: Fusing heterogeneous tri-dimensional information for reconstructing submerged structures in harsh sub-sea environments. SSRN (2023). https:\/\/doi.org\/10.2139\/ssrn.4409685","journal-title":"SSRN"},{"key":"13_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-3-030-50347-5_33","volume-title":"Image Analysis and Recognition","author":"PN Leite","year":"2020","unstructured":"Leite, P.N., Silva, R.J., Campos, D.F., Pinto, A.M.: Dense disparity maps from RGB and sparse depth information using deep regression models. In: Campilho, A., Karray, F., Wang, Z. (eds.) ICIAR 2020. LNCS, vol. 12131, pp. 379\u2013392. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50347-5_33"},{"issue":"2","key":"13_CR16","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","volume":"28","author":"S Lloyd","year":"1982","unstructured":"Lloyd, S.: Least squares quantization in pcm. IEEE Trans. Inf. Theory 28(2), 129\u2013137 (1982). https:\/\/doi.org\/10.1109\/TIT.1982.1056489","journal-title":"IEEE Trans. Inf. Theory"},{"key":"13_CR17","doi-asserted-by":"publisher","unstructured":"Mary Synthuja Jain\u00a0Preetha, M., Padma\u00a0Suresh, L., John\u00a0Bosco, M.: Image segmentation using seeded region growing. In: 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET), pp. 576\u2013583 (2012). https:\/\/doi.org\/10.1109\/ICCEET.2012.6203897","DOI":"10.1109\/ICCEET.2012.6203897"},{"key":"13_CR18","doi-asserted-by":"publisher","unstructured":"Pinto, A.M., et al.: Atlantis coastal testbed: a near-real playground for the testing and validation of robotics for o &m. In: OCEANS 2023 - Limerick, pp.\u00a01\u20135 (2023). https:\/\/doi.org\/10.1109\/OCEANSLimerick52467.2023.10244595","DOI":"10.1109\/OCEANSLimerick52467.2023.10244595"},{"key":"13_CR19","doi-asserted-by":"publisher","unstructured":"Pinto, A.M., et al.: Atlantis - the atlantic testing platform for maritime robotics. In: OCEANS 2021, San Diego - Porto, pp.\u00a01\u20135 (2021). https:\/\/doi.org\/10.23919\/OCEANS44145.2021.9706059","DOI":"10.23919\/OCEANS44145.2021.9706059"},{"key":"13_CR20","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.inffus.2019.07.014","volume":"55","author":"AM Pinto","year":"2020","unstructured":"Pinto, A.M., Matos, A.C.: Maresye: a hybrid imaging system for underwater robotic applications. Inform. Fusion 55, 16\u201329 (2020). https:\/\/doi.org\/10.1016\/j.inffus.2019.07.014","journal-title":"Inform. Fusion"},{"key":"13_CR21","doi-asserted-by":"publisher","unstructured":"Pinto, A.M., Pinto, H., Matos, A.C.: A mosaicking approach for visual mapping of large-scale environments. In: 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 87\u201393 (2016). https:\/\/doi.org\/10.1109\/ICARSC.2016.18","DOI":"10.1109\/ICARSC.2016.18"},{"key":"13_CR22","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":"13_CR23","doi-asserted-by":"publisher","unstructured":"Silva, R., Matos, A., Pinto, A.M.: Multi-criteria metric to evaluate motion planners for underwater intervention. Auton, Robots 46, 971\u2013983 (12 2022). https:\/\/doi.org\/10.1007\/s10514-022-10060-x","DOI":"10.1007\/s10514-022-10060-x"},{"key":"13_CR24","doi-asserted-by":"publisher","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv (2015). https:\/\/doi.org\/10.48550\/arXiv.1409.1556","DOI":"10.48550\/arXiv.1409.1556"},{"key":"13_CR25","doi-asserted-by":"publisher","unstructured":"Skliris, N., Marsh, R., Srokosz, M., Aksenov, Y., Rynders, S., Fournier, N.: Assessing extreme environmental loads on offshore structures in the north sea from high-resolution ocean currents, waves and wind forecasting. J. Marine Sci. Eng. 9(10) (2021). https:\/\/doi.org\/10.3390\/jmse9101052","DOI":"10.3390\/jmse9101052"},{"key":"13_CR26","doi-asserted-by":"publisher","unstructured":"Zhou, Y., Wang, J., Li, B., Meng, Q., Rocco, E., Saiani, A.: Underwater scene segmentation by deep neural network. In: UK-RAS19 Conference: \u201cEmbedded Intelligence: Enabling & Supporting RAS Technologies\u201d Proceedings (2019). https:\/\/doi.org\/10.31256\/UKRAS19.12","DOI":"10.31256\/UKRAS19.12"}],"container-title":["Lecture Notes in Networks and Systems","Robot 2023: Sixth Iberian Robotics Conference"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-59167-9_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T07:10:47Z","timestamp":1714115447000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-59167-9_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031591662","9783031591679"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-59167-9_13","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"27 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ROBOT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberian Robotics conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Coimbra","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"robot2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iberianroboticsconf.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}