Demo Data from EDBT 2026 / reviewers in the wild / expert
Maik Bollmann
dblp:11/3378
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2001
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2Graphics, 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 · 46% Deep learning architectures and training · 23% Image recognition and object detection · 23% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping
active vision |
0.0 | 1 | 2001 | Data- and Model-Driven Gaze Control for an Active-Vision System · IEEE Trans. Pattern Anal. Mach. Intell. 2001 |
Machine learning › Deep learning architectures and training
attention control |
0.0 | 1 | 2001 | Data- and Model-Driven Gaze Control for an Active-Vision System · IEEE Trans. Pattern Anal. Mach. Intell. 2001 |
Robotics › Robot navigation and mapping › active vision
gaze control |
0.0 | 1 | 2001 | Data- and Model-Driven Gaze Control for an Active-Vision System · IEEE Trans. Pattern Anal. Mach. Intell. 2001 |
Computer vision › Image recognition and object detection
visual attention modeling |
0.0 | 1 | 2001 | Data- and Model-Driven Gaze Control for an Active-Vision System · IEEE Trans. Pattern Anal. Mach. Intell. 2001 |
Computer vision › 3D vision › 3d scene understanding
dynamic scene understanding |
0.0 | 1 | 2001 | Data- and Model-Driven Gaze Control for an Active-Vision System · IEEE Trans. Pattern Anal. Mach. Intell. 2001 |
Methods — techniques the papers use, named apart from their topics
model-driven model · 0.0data-driven model · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | Data- and Model-Driven Gaze Control for an Active-Vision SystemabstractModels of visual attention provide a general approach to control the activities of active vision systems. We introduce a new model of attentional control that differs in important aspects from conventional ones. We divide the selection into two stages, which is more suitable for the system as well as explaining different phenomena found in natural visual attention, such as the dispute between early and late selection. The proposed model is especially designed for use in dynamic scenes. Our approach aims at modeling as much of a general active vision system as possible and designing clean interfaces for the integration of the remaining specific aspects needed in order to solve specific problems. Gerriet Backer, Bärbel Mertsching, Maik Bollmann |
IEEE Trans. Pattern Anal. Mach. Intell. | 3 |
| 1999 | Playing Domino: A Case Study for an Active Vision System
Maik Bollmann, Rainer Hoischen, Michael Jesikiewicz, Christoph Justkowski, Bärbel Mertsching |
ICVS | 1 |
| 1997 | Visual Attention and Gaze Control for an Active Vision System
Bärbel Mertsching, Maik Bollmann |
ICONIP (1) | 2 |