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Birger Streckel

dblp:12/1020 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2005
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 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.

Computer graphics and multimedia
1 paper
Virtual and augmented reality · 87% Visualization and visual analytics · 13%

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

TopicWeightPapersLastEvidence papers
Virtual and augmented reality › tracking
augmented reality tracking
0.112005
Lens Model Selection for a Markerless AR Tracking System · ISMAR 2005
Virtual and augmented reality › tracking
markerless tracking
0.112005
Lens Model Selection for a Markerless AR Tracking System · ISMAR 2005
Visualization and visual analytics › focus+context visualization
fisheye lens
0.012005
Lens Model Selection for a Markerless AR Tracking System · ISMAR 2005

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

structure from motion · 0.12d point tracking · 0.1
YearPublicationVenuePosition
2005 Lens Model Selection for a Markerless AR Tracking System
abstract
This work describes a visual markerless real-time tracking system for augmented reality applications. The system uses a firewire camera with a fisheye lens mounted at 10 fps. Visual tracking of 3D scene points is performed simultaneously with 3D camera pose estimation without any prior scene knowledge. All visual-geometric data is acquired using a structure-from-motion approach. The lens selection was driven by research results that show the superiority of a fisheye lens to a standard perspective lens for this approach. 2D features in the hemispherical image are tracked using a 2D point tracker. Based on the feature tracks, 3D camera ego-motion and 3D features are estimated.
Birger Streckel, Jan-Friso Evers-Senne, Reinhard Koch
ISMAR1