Cristin-prosjekt-ID: 2522411
Sist endret: 23. november 2021, 12:23

Cristin-prosjekt-ID: 2522411
Sist endret: 23. november 2021, 12:23
Prosjekt

Visual Intelligence

prosjektleder

Robert Jenssen
ved Institutt for fysikk og teknologi ved UiT Norges arktiske universitet

prosjekteier / koordinerende forskningsansvarlig enhet

  • Institutt for fysikk og teknologi ved UiT Norges arktiske universitet

Finansiering

  • TotalbudsjettNOK 96.000
  • Norges forskningsråd
    Prosjektkode: 309439

Klassifisering

Vitenskapsdisipliner

Datateknologi • Informasjons- og kommunikasjonssystemer • Algoritmer og beregnbarhetsteori • Medisinsk teknologi • Annen marin teknologi • Matematisk modellering og numeriske metoder • Annen informasjonsteknologi • Simulering, visualisering, signalbehandling, bildeanalyse • Fiskeriteknologi • Marin teknologi

Kategorier

Prosjektkategori

  • Anvendt forskning
  • Bidragsprosjekt

Kontaktinformasjon

Tidsramme

Aktivt
Start: 1. desember 2020 Slutt: 30. november 2028

Beskrivelse Beskrivelse

Tittel

Visual Intelligence

Populærvitenskapelig sammendrag

Imaging of the unseen is needed in a large variety of applications: imaging the inside of the body, the seawaters, the subsea and the earth from above. However, there is a lack of analysis tools with the power and trustworthiness to fully exploit such complex imagery for knowledge extraction, innovations, and reliable new technology.

Vision

Visual Intelligence shall be the lead provider of cutting-edge solutions for complex image analysis by leveraging deep learning to answer innovation needs shared across a consortium of corporate and public user partners from different business areas. They all rely on complex image data for sustainable value creation, posing shared research challenges. This enables crucial cross-fertilization in the research and innovation.

Knowledge needs

A big driver in the recent progress of AI systems is the use of deep learning. The largest success has been within computer vision, where the use of deep learning has led to a giant leap in performance for image object detection and recognition. The progress has been immense in general computer vision applications for images taken from handheld optical cameras, facilitated by vast datasets collected by actors like Google or Facebook. This has led to a range of new image-based technologies that is rapidly changing society. Despite these advances, it is still a long way before the potential of deep learning for visual intelligence is realized for applications and industries relying on more complex visual data.

The innovation gap between current state-of-the-art and its potential is particularly large for user applications where the amount of annotated visual data is limited, and experts are needed to interpret the data. The Visual Intelligence research partners, UiT – The Arctic University of Norway, Norsk Regnesentral (NR, Norwegian Computing Center) and the University of Oslo (UiO), will answer these challenges. Creating synergies and cross-fertilization between different applications that all depend on complex visual data to enable new deep learning methodology, we will contribute to realize this potential for four core selected innovation areas of great societal importance:

  • medicine and health
  • marine science
  • energy and industry
  • earth observation.

Visual Intelligence includes the following major private and public partners: Equinor, GE Vingmed Ultrasound, the Cancer Registry, University Hospital of North Norway (UNN), Helse Nord IKT, Institute of Marine Research (IMR), Kongsberg Satellite Services (KSAT), and Terratec. Together we will enable visual intelligence for extracting crucial and actionable knowledge from complex visual data. Visual Intelligence will focus on deep learning research challenges shared by the different application domains, enabling translation of solutions between domains.

 

Objectives

Our main objective is to unlock the potential of visual intelligence across the main innovation areas medicine and health, marine science, energy sector, and earth observation by enabling the next generation deep learning methodology for extracting knowledge from complex image data.

The secondary objectives aim at this through analysis of complex imagery for real-life applications with:

  • Solutions for learning from limited data
  • Solutions for exploitation of context, dependencies, and prior knowledge
  • Solutions for estimation of confidence and quantification of uncertainties
  • Solutions for explainable and reliable predictions

The innovations will result in new and improved products and services for value creation for the user partners.

 

Vitenskapelig sammendrag

 

 

prosjektdeltakere

prosjektleder
Aktiv cristin-person

Robert Jenssen

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

Line Eikvil

  • Tilknyttet:
    Lokalt ansvarlig
    ved Statistisk analyse, maskinlæring og bildeanalyse SAMBA ved Norsk Regnesentral

Anne H Schistad Solberg

  • Tilknyttet:
    Lokalt ansvarlig
    ved Digital signalbehandling og bildeanalyse ved Universitetet i Oslo

Inger Solheim

  • Tilknyttet:
    Prosjektdeltaker
    ved Fakultet for naturvitenskap og teknologi ved UiT Norges arktiske universitet
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Resultater Resultater

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

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