Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from EDBT 2026 / reviewers in the wild / expert

R. Billert

dblp:29/2874 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 1

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 · 70% Multimedia analysis and retrieval · 30%

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

TopicWeightPapersLastEvidence papers
Virtual and augmented reality › tracking
markerless tracking		 0.012003
Marker-less Vision Based Tracking for Mobile Augmented Reality · ISMAR 2003
Multimedia analysis and retrieval › object recognition
model-based recognition		 0.012003
Marker-less Vision Based Tracking for Mobile Augmented Reality · ISMAR 2003
Virtual and augmented reality
tracking and registration		 0.012003
Marker-less Vision Based Tracking for Mobile Augmented Reality · ISMAR 2003
Virtual and augmented reality › augmented reality
mobile augmented reality		 0.012003
Marker-less Vision Based Tracking for Mobile Augmented Reality · ISMAR 2003

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

3d hypothesis generation · 0.02d graph matching · 0.0
YearPublicationVenuePosition
2003 Marker-less Vision Based Tracking for Mobile Augmented Reality
abstract
In this paper an object recognition and tracking approach for the mobile, marker-less and PDA-based augmented reality system AR-PDA is described. For object recognition and localization 2D features are extracted from images and compared with a priori known 3D models. The system consists of a 2D graph matching, 3D hypothesis generation and validation and an additional texture based validation step.
Daniel Beier, R. Billert, Beat D. Brüderlin, Dirk Stichling, Bernd Kleinjohann
 ISMAR2