Agnar Holten Sivertsen

ORCID: 0000-0003-3905-130X
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About
Contact & Profiles
Research Areas
  • Spectroscopy and Chemometric Analyses
  • Meat and Animal Product Quality
  • Advanced Chemical Sensor Technologies
  • Cryospheric studies and observations
  • Identification and Quantification in Food
  • Climate change and permafrost
  • Atmospheric and Environmental Gas Dynamics
  • Arctic and Antarctic ice dynamics
  • Parasite Biology and Host Interactions
  • Arctic and Russian Policy Studies
  • Methane Hydrates and Related Phenomena
  • Marine animal studies overview
  • 3D Surveying and Cultural Heritage
  • Robotics and Sensor-Based Localization
  • Social and Educational Sciences
  • Water Quality Monitoring Technologies
  • Education, Healthcare and Sociology Research
  • Oil Spill Detection and Mitigation
  • Spectroscopy Techniques in Biomedical and Chemical Research
  • Neural Networks and Applications
  • Industrial Vision Systems and Defect Detection
  • Bee Products Chemical Analysis
  • Drilling and Well Engineering
  • Wine Industry and Tourism
  • Oil and Gas Production Techniques

NORCE Norwegian Research Centre
2020-2022

Northern Research Institute
2019

Nofima
2004-2013

Nofi Tromsø (Norway)
2010

UiT The Arctic University of Norway
2010

Tromsø research foundation
2008

ABSTRACT: A promising method for detection of parasites in whitefish fillets has been developed. By use imaging spectroscopy it is possible to record both spectral and spatial information from an object. In this work shown that by applying a white light transmission setup cod ( Gadus morhua ) fillets, make images containing differentiate between fish muscle parasites. The are analyzed discriminant partial least square regression as well image‐filtering techniques. identifies on the surface...

10.1111/j.1750-3841.2006.00212.x article EN Journal of Food Science 2007-01-01

Abstract: Traditional quality control of cod fillets is currently made by manual inspection on candling tables. This a time consuming and expensive operation, contributing to significant share the cost with fillet production. In this study, transillumination hyperspectral imaging was implemented as method for automatic nematode detection in moving conveyer belt, evaluated industrially processed fillets. An overall rate 58% all nematodes ( N = 922), 71% 46% dark pale nematodes, respectively,...

10.1111/j.1750-3841.2010.01928.x article EN Journal of Food Science 2010-12-01

The Arctic is a region undergoing continuous and significant changes in land relief due to different glaciological, geomorphological hydrogeological processes. To study those phenomena, digital elevation models (DEMs) highly accurate maps with high spatial resolution are of prime importance. In this work, we assess the accuracy high-resolution photogrammetric DEMs orthomosaics derived from aerial images captured 2020 over Hornsund, Svalbard. Further, demonstrate generated using point clouds...

10.3390/rs14030601 article EN cc-by Remote Sensing 2022-01-26

Svalbard Integrated Arctic Earth Observing System (SIOS) is an international partnership of research institutions studying the environment and climate in around Svalbard. SIOS developing efficient observing system, where researchers share technology, experience, data, work together to close knowledge gaps, decrease environmental footprint science. maintains facilitates various scientific activities such as State Environmental Science (SESS) report, access infrastructure Svalbard, observation...

10.3390/rs13040712 article EN cc-by Remote Sensing 2021-02-15

Optical image sensors are the most common remote sensing data acquisition devices present in Unmanned Aerial Systems (UAS). In this context, assigning a location geographic frame of reference to acquired is necessary task majority applications. This process denominated direct georeferencing when ground control points not used. Despite it applies simple mathematical fundamentals, complete involves much information, such as camera sensor characteristics, mounting measurements, attitude and...

10.3390/s22020604 article EN cc-by Sensors 2022-01-13

This paper presents the development of a virtual reality simulation environment for Unmanned Aerial Systems (UAS) solar plant inspection. The objective this work is to provide tool test autonomous inspection and computer vision algorithms generate realistic synthetic data deep learning. These techniques demand data, which can be made available by high-quality graphics engines, such as ones used game development. In work, Unreal Engine 4 host plant. panels were modeled using Blender...

10.1109/airpharo52252.2021.9571060 article EN 2021-10-01
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