Cristin-prosjekt-ID: 627070
Sist endret: 26. november 2021, 13:34

Cristin-prosjekt-ID: 627070
Sist endret: 26. november 2021, 13:34
Prosjekt

COGMAR: Ubiquitous cognitive computer vision for marine services

prosjektleder

Arnt-Børre Salberg
ved Norsk Regnesentral

prosjekteier / koordinerende forskningsansvarlig enhet

  • Norsk Regnesentral

Finansiering

  • Norges forskningsråd
    Prosjektkode: 270966

Klassifisering

Emneord

Deep learning

Kategorier

Prosjektkategori

  • Anvendt forskning
  • Doktorgradsprosjekt
  • Grunnforskning

Kontaktinformasjon

Sted
Arnt-Børre Salberg

Tidsramme

Aktivt
Start: 1. april 2017 Slutt: 30. juni 2022

Beskrivelse Beskrivelse

Tittel

COGMAR: Ubiquitous cognitive computer vision for marine services

Vitenskapelig sammendrag

Deep learning has been called the revolutionary technique that quietly changed machine vision forever, but is at present mainly applicable to standard RGB images of natural scenes or objects, or otherwise only for other types of imagery when a substantial amount of labelled data is available, which is seldom the case.

This project aims at enabling this technology for computer vision problems anywhere, by developing easy-to-use cognitive solutions also for non-standard images and thereby extending the use of autonomous cognitive computer vision systems to new application areas. The methodology will be general and transferable to other domains such as medical imagery, remote sensing and various industrial applications. Within the project the aim is to solve key big data computer vision challenges in the marine sector.

The overall concept of the project is to exploit the power of deep convolutional neural networks (CNNs) by developing new solutions for learning necessary to classify, localize and segment objects in non-standard, sparsely labelled, image data. Motivated by the methods' ability to generalize and the fact that unlabelled data is often inexpensive to acquire, our approach for solving this will be based on three main concepts; (i) cross-domain transfer learning, (ii) semi-supervised learning and (iii) data augmentation and simulation.

Fisheries and aquaculture are major industries in Norway, and marine image data are acquired in a wide range of formats and modalities for various tasks. Automatic solutions for extracting information from these big non-standard image data will bring exploitation of these data in the marine science to a new level, enabling extraction of new knowledge and continuous monitoring of marine ecosystems. Solutions from the project will also contribute to innovation for industries manufacturing solutions for automated monitoring of fish and marine environments.

prosjektdeltakere

prosjektleder

Arnt-Børre Salberg

  • Tilknyttet:
    Prosjektleder
    ved Norsk Regnesentral

Ingrid Utseth

  • Tilknyttet:
    Prosjektdeltaker
    ved Norsk Regnesentral

Changkyu Choi

  • Tilknyttet:
    Prosjektdeltaker
    ved Institutt for fysikk og teknologi ved UiT Norges arktiske universitet

Alba Ordonez

  • Tilknyttet:
    Prosjektdeltaker
    ved Norsk Regnesentral

Rama Chellappa

  • Tilknyttet:
    Prosjektdeltaker
    ved University of Maryland at College Park
1 - 5 av 13 | Neste | Siste »

Resultater Resultater

Using model averaging ensembles in semantic segmentation of marine echosounder data for acoustic classification of species .

Ordonez, Alba; Utseth, Ingrid; Eikvil, Line; Handegard, Nils Olav. 2021, NOBIM 2021. NR, HAVFORSKVitenskapelig foredrag

Improving marine acoustic target classification with context information.

Utseth, Ingrid; Ordonez, Alba; Eikvil, Line; Brautaset, Olav; Salberg, Arnt-Børre; Handegard, Nils Olav. 2021, Visual Intelligence Days 2021. NR, HAVFORSKPoster

Deep domain adaptation applied to automatic fish age prediction.

Ordonez, Alba; Harbitz, Alf; Elvarsson, Bjarki; Eikvil, Line; Salberg, Arnt-Børre. 2021, Visual Intelligence workshop on learning from limited data. NR, HAVFORSKVitenskapelig foredrag

Marine Acoustic Classification: Supervised Semantic Segmentation of Echosounder Data using CNNs.

Brautaset, Olav. 2021, Visual Intelligence Seminar series. NRVitenskapelig foredrag

Semi-supervised Semantic Segmentation in Multi-frequency Echosounder Data.

Choi, Changkyu; Kampffmeyer, Michael; Handegard, Nils Olav; Salberg, Arnt Børre; Eikvil, Line; Jenssen, Robert. 2021, SFI Visual Intelligence days 2021 . UIT, NR, HAVFORSKPoster
1 - 5 av 36 | Neste | Siste »