Blar i Brage IMR på forfatter "Knausgård, Kristian Muri"

Viser treff 1-5 av 5

    • A contrastive learning approach for individual re-identification in a wild fish population 

      Olsen, Ørjan Langøy; Sørdalen, Tonje Knutsen; Goodwin, Morten; Malde, Ketil; Knausgård, Kristian Muri; Halvorsen, Kim Aleksander Tallaksen (Peer reviewed; Journal article, 2023)
      In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly being replaced by individual re-identification ...

    • Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation 

      Olsvik, Erlend; Trinh, Christian M. D.; Knausgård, Kristian Muri; Wiklund, Arne; Sørdalen, Tonje Knutsen; Kleiven, Alf Ring; Lei, Jiao; Goodwin, Morten (Peer reviewed; Journal article, 2019)

    • Temperate fish detection and classification: a deep learning based approach 

      Knausgård, Kristian Muri; Wiklund, Arne; Sørdalen, Tonje Knutsen; Halvorsen, Kim Aleksander Tallaksen; Kleiven, Alf Ring; Lei, Jiao; Goodwin, Morten (Peer reviewed; Journal article, 2021)
      A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize ...

    • Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook 

      Goodwin, Morten; Halvorsen, Kim Aleksander Tallaksen; Jiao, Lei; Knausgård, Kristian Muri; Martin, Angela Helen; Moyano, Marta; Oomen, Rebekah Alice; Rasmussen, Jeppe Have; Sørdalen, Tonje Knutsen; Thorbjørnsen, Susanna Huneide (Peer reviewed; Journal article, 2022)
      The deep learning (DL) revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. New methods provide analysis of data from ...

    • Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook 

      Goodwin, Morten; Halvorsen, Kim Aleksander Tallaksen; Jiao, Lei; Knausgård, Kristian Muri; Martin, Angela Helen; Moyano, Marta; Oomen, Rebekah Alice; Rasmussen, Jeppe Have; Sørdalen, Tonje Knutsen; Thorbjørnsen, Susanna Huneide (Peer reviewed; Journal article, 2022)
      The deep learning (DL) revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. New methods provide analysis of data from ...