Cristin-resultat-ID: 1873231
Sist endret: 18. januar 2021, 18:01
NVI-rapporteringsår: 2020
Resultat
Vitenskapelig artikkel
2020

Towards detection and classification of microscopic foraminifera using transfer learning

Bidragsytere:
  • Thomas Haugland Johansen og
  • Steffen Aagaard Sørensen

Tidsskrift

Proceedings of the Northern Lights Deep Learning Workshop
e-ISSN 2703-6928
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2020
Volum: 1
Open Access

Beskrivelse Beskrivelse

Tittel

Towards detection and classification of microscopic foraminifera using transfer learning

Sammendrag

Foraminifera are single-celled marine organisms, which may have a planktic or benthic lifestyle. During their life cycle they construct shells consisting of one or more chambers, and these shells remain as fossils in marine sediments. Classifying and counting these fossils have become an important tool in e.g. oceanography and climatology. Currently the process of identifying and counting microfossils is performed manually using a microscope and is very time consuming. Developing methods to automate this process is therefore considered important across a range of research fields. The first steps towards developing a deep learning model that can detect and classify microscopic foraminifera are proposed. The proposed model is based on a VGG16 model that has been pretrained on the ImageNet dataset, and adapted to the foraminifera task using transfer learning. Additionally, a novel image dataset consisting of microscopic foraminifera and sediments from the Barents Sea region is introduced.

Bidragsytere

Aktiv cristin-person

Thomas Haugland Johansen

  • Tilknyttet:
    Forfatter
    ved Institutt for matematikk og statistikk ved UiT Norges arktiske universitet

Steffen Aagaard Sørensen

  • Tilknyttet:
    Forfatter
    ved Institutt for geovitenskap ved UiT Norges arktiske universitet
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