Blar i Brage IMR på forfatter "Eikvil, Line"

Viser treff 1-7 av 7

    • Acoustic classification in multifrequency echosounder data using deep convolutional neural networks 

      Brautaset, Olav; Waldeland, Anders Ueland; Johnsen, Espen; Malde, Ketil; Eikvil, Line; Salberg, Arnt-Børre; Handegard, Nils Olav (Peer reviewed; Journal article, 2020)
      Acoustic target classification is the process of assigning observed acoustic backscattering intensity to an acoustic category. A deep learning strategy for acoustic target classification using a convolutional network is ...

    • Automatic Fish Age Determination across Different Otolith Image Labs Using Domain Adaptation 

      Ordonez, Alba; Eikvil, Line; Salberg, Arnt-Børre; Harbitz, Alf; Elvarsson, Bjarki Thor (Peer reviewed; Journal article, 2022)
      The age determination of fish is fundamental to marine resource management. This task is commonly done by analysis of otoliths performed manually by human experts. Otolith images from Greenland halibut acquired by the ...

    • Explaining decisions of deep neural networks used for fish age prediction 

      Ordonez, Alba; Eikvil, Line; Salberg, Arnt-Børre; Harbitz, Alf; Murray, Sean Meling; Kampffmeyer, Michael (Peer reviewed; Journal article, 2020)
      Age-reading of fish otoliths (ear stones) is important for the sustainable management of fish resources. However, the procedure is challenging and requires experienced readers to carefully examine annual growth zones. In ...

    • Machine intelligence and the data-driven future of marine science 

      Malde, Ketil; Handegard, Nils Olav; Eikvil, Line; Salberg, Arnt Børre (Peer reviewed; Journal article, 2019)
      Oceans constitute over 70% of the earth’s surface, and the marine environment and ecosystems are central to many global challenges. Not only are the oceans an important source of food and other resources, but they also ...

    • Machine Learning + Marine Science: Critical Role of Partnerships in Norway 

      Handegard, Nils Olav; Eikvil, Line; Jenssen, Robert; Kampffmeyer, Michael; Salberg, Arnt Børre; Malde, Ketil (Others, 2021)
      In this essay, we review some recent advances in developing machine learning (ML) methods for marine science applications in Norway. We focus mostly on deep learning (DL) methods and review the challenges we have faced in ...

    • Semi-supervised target classification in multi-frequency echosounder data 

      Choi, Changkyu; Kampffmeyer, Michael; Handegard, Nils Olav; Salberg, Arnt Børre; Brautaset, Olav; Eikvil, Line; Jenssen, Robert (Peer reviewed; Journal article, 2021)
      Acoustic target classification in multi-frequency echosounder data is a major interest for the marine ecosystem and fishery management since it can potentially estimate the abundance or biomass of the species. A key problem ...

    • Smart Fisheries in Norway: Partnership between Science, Technology, and the Fishing Sector 

      Handegard, Nils Olav; Algrøy, Tonny; Eikvil, Line; Hammersland, Hege; Tenningen, Maria; Ona, Egil (Journal article, 2021)