{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:16:12Z","timestamp":1743084972486,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031539596"},{"type":"electronic","value":"9783031539602"}],"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\/978-3-031-53960-2_10","type":"book-chapter","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T05:54:30Z","timestamp":1710914070000},"page":"142-150","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Detection and\u00a0Spline-Based Pixel-Length Estimation of\u00a0Fishes from\u00a0Images"],"prefix":"10.1007","author":[{"given":"Rajarshi","family":"Biswas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcel","family":"Mutz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rana","family":"Khonsari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dirk","family":"Werth","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"10_CR1","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1007\/978-3-031-37717-4_72","volume-title":"Intelligent Computing - SAI 2023","author":"R Biswas","year":"2023","unstructured":"Biswas, R., Mutz, M., Khonsari, R., Werth, D.: A study on artificial intelligence techniques for automatic fish-size estimation. In: Arai, K. (ed.) SAI 2023. LNNS, vol. 711, pp. 116\u2013126. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-37717-4_72"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Bradley, D., Merrifield, M., Miller, K.M., Lomonico, S., Wilson, J.R., Gleason, M.G.: Opportunities to improve fisheries management through innovative technology and advanced data systems. Fish Fisheries 20(3), 564\u2013583 (2019)","DOI":"10.1111\/faf.12361"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., Culurciello, E.: Linknet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1\u20134. IEEE (2017)","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Garcia, R., et al.: Automatic segmentation of fish using deep learning with application to fish size measurement. ICES J. Marine Sci. 77(4), 1354\u20131366 (2019)","DOI":"10.1093\/icesjms\/fsz186"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Garcia-d\u2019Urso, N., et al.: The deepfish computer vision dataset for fish instance segmentation, classification, and size estimation. Sci. Data 9(1), 287 (2022)","DOI":"10.1038\/s41597-022-01416-0"},{"issue":"1","key":"10_CR6","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","volume":"38","author":"R Girshick","year":"2015","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142\u2013158 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"10_CR8","unstructured":"Jocher, G., et al.:. ultralytics\/yolov5: v6.2 - YOLOv5 Classification Models, Apple M1, Reproducibility, ClearML and Deci.ai integrations, August 2022"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Konovalov, D.A., Saleh, A., Efremova, D.B., Domingos, J.A., Jerry, D.R.: Automatic weight estimation of harvested fish from images. In: 2019 Digital Image Computing: Techniques and Applications (DICTA), pp. 1\u20137. IEEE (2019)","DOI":"10.1109\/DICTA47822.2019.8945971"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Le\u00a0Cren, E.D.: The length-weight relationship and seasonal cycle in gonad weight and condition in the perch (perca fluviatilis). J. Animal Ecol. 201\u2013219 (1951)","DOI":"10.2307\/1540"},{"key":"10_CR11","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":"10_CR12","doi-asserted-by":"publisher","first-page":"08","DOI":"10.1111\/2041-210X.13282","volume":"10","author":"G Monkman","year":"2019","unstructured":"Monkman, G., Hyder, K., Kaiserc, M., Vidal, F.: Using machine vision to estimate fish length from images using regional convolutional neural networks. Methods Ecol. Evol. 10, 08 (2019)","journal-title":"Methods Ecol. Evol."},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Monkman, G.G., Hyder, K., Kaiser, M.J., Vidal, F.P.: Accurate estimation of fish length in single camera photogrammetry with a fiducial marker. ICES J. Marine Sci. 77(6), 2245\u20132254 (2019)","DOI":"10.1093\/icesjms\/fsz030"},{"key":"10_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2019.136044","volume":"706","author":"EA O\u2019Neill","year":"2020","unstructured":"O\u2019Neill, E.A., Stejskal, V., Clifford, E., Rowan, N.J.: Novel use of peatlands as future locations for the sustainable intensification of freshwater aquaculture production - a case study from the republic of Ireland. Sci. Total Environ. 706, 136044 (2020)","journal-title":"Sci. Total Environ."},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Palmer, M., Ellacur\u00eda, A.\u00c1., Molt\u00f3, V., Catal\u00e1n, I.A.: Automatic, operational, high-resolution monitoring of fish length and catch numbers from landings using deep learning. Fisher. Res. 246, 106166 (2022)","DOI":"10.1016\/j.fishres.2021.106166"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Rauf, H.T., Lali, M.I.U., Zahoor, S., Shah, S.Z.H., Rehman, A.U., Bukhari, S.A.C.: Visual features based automated identification of fish species using deep convolutional neural networks. Comput. Electron. Agric. 167, 105075 (2019)","DOI":"10.1016\/j.compag.2019.105075"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Rosen, S., J\u00f6rgensen, T., Hammersland-White, D., Holst, J.C.: Deepvision: a stereo camera system provides highly accurate counts and lengths of fish passing inside a trawl. Can. J. Fisher. Aquatic Sci. 70(10), 1456\u20131467 (2013)","DOI":"10.1139\/cjfas-2013-0124"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Saleh, A., Laradji, I.H., Konovalov, D.A., Bradley, M., Vazquez, D., Sheaves M.: A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis. Sci. Rep. 10(1), 14671 (2020)","DOI":"10.1038\/s41598-020-71639-x"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Siddiqui, S.A., et al.: Automatic fish species classification in underwater videos: exploiting pre-trained deep neural network models to compensate for limited labelled data. ICES J. Marine Sci. 75(1), 374\u2013389 (2018)","DOI":"10.1093\/icesjms\/fsx109"},{"key":"10_CR20","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.biosystemseng.2019.11.002","volume":"189","author":"C-H Tseng","year":"2020","unstructured":"Tseng, C.-H., Hsieh, C.-L., Kuo, Y.-F.: Automatic measurement of the body length of harvested fish using convolutional neural networks. Biosyst. Eng. 189, 36\u201347 (2020)","journal-title":"Biosyst. Eng."},{"issue":"22","key":"10_CR21","doi-asserted-by":"publisher","first-page":"2882","DOI":"10.3390\/electronics10222882","volume":"10","author":"TTE Vo","year":"2021","unstructured":"Vo, T.T.E., Ko, H., Huh, J.-H., Kim, Y.: Overview of smart aquaculture system: focusing on applications of machine learning and computer vision. Electronics 10(22), 2882 (2021)","journal-title":"Electronics"},{"key":"10_CR22","doi-asserted-by":"publisher","first-page":"2785","DOI":"10.1007\/s11831-020-09486-2","volume":"28","author":"L Yang","year":"2021","unstructured":"Yang, L., et al.: Computer vision models in intelligent aquaculture with emphasis on fish detection and behavior analysis: a review. Arch. Comput. Methods Eng. 28, 2785\u20132816 (2021)","journal-title":"Arch. Comput. Methods Eng."},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Yang, Y., Xue, B., Jesson, L., Wylie, M., Zhang, M., Wellenreuther, M.: Deep convolutional neural networks for fish weight prediction from images. In: 2021 36th International Conference on Image and Vision Computing New Zealand (IVCNZ), pp. 1\u20136. IEEE (2021)","DOI":"10.1109\/IVCNZ54163.2021.9653412"},{"issue":"1","key":"10_CR24","doi-asserted-by":"publisher","first-page":"6243","DOI":"10.1038\/s41598-017-06538-9","volume":"7","author":"C Zhou","year":"2017","unstructured":"Zhou, C., et al.: An adaptive image enhancement method for a recirculating aquaculture system. Sci. Rep. 7(1), 6243 (2017)","journal-title":"Sci. Rep."},{"key":"10_CR25","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.compag.2017.02.013","volume":"135","author":"C Zhou","year":"2017","unstructured":"Zhou, C., et al.: Near-infrared imaging to quantify the feeding behavior of fish in aquaculture. Comput. Electron. Agric. 135, 233\u2013241 (2017)","journal-title":"Comput. Electron. Agric."}],"container-title":["Lecture Notes in Networks and Systems","Advances in Information and Communication"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53960-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T05:57:31Z","timestamp":1710914251000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53960-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031539596","9783031539602"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53960-2_10","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":"21 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FICC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Future of Information and Communication Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Berlin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","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":"4 April 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 April 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ficc2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/saiconference.com\/FICC","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}