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Göran Olofsson

dblp:62/1560 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 1997
0000-0003-3747-7120ORCID · corroborated

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

Artificial intelligence and machine learning · 3Graphics, computer vision, multimedia, augmented reality and games · 2

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
2 papers
Robot navigation and mapping · 39% Video understanding and tracking · 30% 3D vision · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › active perception
active object recognition		 0.011994
Active Object Recognition Integrating Attention and Viewpoint Control · ECCV (2) 1994
Computer vision › 3D vision › 3d shape representation
aspect graph		 0.011994
Qualitative tracking of 3-D objects using active contour networks · CVPR 1994
Computer vision › Video understanding and tracking
object tracking		 0.011994
Qualitative tracking of 3-D objects using active contour networks · CVPR 1994
Robotics › Robot navigation and mapping › active vision
viewpoint control		 0.011994
Active Object Recognition Integrating Attention and Viewpoint Control · ECCV (2) 1994

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

viewpoint control · 0.0attention · 0.0aspect graph clustering · 0.0active contour network · 0.0
YearPublicationVenuePosition
1997 Active Object Recognition Integrating Attention and Viewpoint Control
Sven J. Dickinson, Henrik I. Christensen, John K. Tsotsos, Göran Olofsson
 Comput. Vis. Image Underst.4
1994 Qualitative tracking of 3-D objects using active contour networks
abstract
In this paper, we track changes in the appearance of the object as it moves from one frame to the next. At a symbolic level, an aspect graph clusters all the views of an object into a set of topologically distinct classes in terms of which surfaces of an object are visible from a given viewpoint (Koenderink and van Doom (1979). Two nodes (or aspects) in the aspect graph are connected by an arc if it is possible to directly move from a viewpoint in which the first aspect is visible to a viewpoint in which the second aspect is visible. Qualitatively, we can envision a tracking strategy which simply tracks an object as it moves from one node to another in the object's aspect graph. Although it does not provide us with accurate pose of the object, it does qualitatively describe the motion of the object without the need for a CAD representation of the object.>
Sven J. Dickinson, Piotr Jasiobedzki, Göran Olofsson, Henrik I. Christensen
 CVPR3
1994 Active Object Recognition Integrating Attention and Viewpoint Control
Sven J. Dickinson, Henrik I. Christensen, John K. Tsotsos, Göran Olofsson
 ECCV (2)4