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Jan O. Hallset

dblp:230/7047 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 1992
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-authorSystems, architecture and hardware · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot navigation and mapping · 56% Image recognition and object detection · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection › object recognition
model-based object recognition
0.011991
Simple vision tracking of pipelines for an autonomous underwater vehicle · ICRA 1991
Robotics › Robot navigation and mapping › mobile robot navigation › vehicle navigation
underwater vehicle navigation
0.011991
Simple vision tracking of pipelines for an autonomous underwater vehicle · ICRA 1991
Robotics › Robot navigation and mapping
localization
0.011991
Simple vision tracking of pipelines for an autonomous underwater vehicle · ICRA 1991

Methods — techniques the papers use, named apart from their topics

sonar sensing · 0.0model-based recognition · 0.0
YearPublicationVenuePosition
1992 A vision system for an autonomous underwater vehicle
abstract
A vision system for the PISCIS project is described. This is a proposed project for the use of an untethered autonomous underwater vehicle (AUV) for pipeline inspection. The vision system is termed PVS (PISCIS vision system). It will assist the AUV in finding and following the pipeline. Robust image interpretation is important as humans can not interact with the vision system and correct errors. The image quality is reduced by backscatter, light absorption, and marine material on the pipelines. The PVS is fixed to a moving vehicle and must rely on a heading sensor and an altitude sonar to match images features with pipeline models. The models are retrieved from a map based on the vehicle's position.>
Jan O. Hallset
ICPR (1)1
1991 Simple vision tracking of pipelines for an autonomous underwater vehicle
abstract
A 2D vision based pipeline tracking system is described for an autonomous underwater vehicle (AUV). A model-based recognition approach is used. From a 2D camera image rectangles are extracted. The real-world size and direction of each rectangle are estimated with the aid of input from a sonar and a compass. The rectangle that is consistent with the diameter and local direction of the pipeline is used to guide the AUV along the pipeline.>
Jan O. Hallset
ICRA1