Gerriet Backer

dblp:32/553 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2002
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 2 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

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping
active vision
0.012001
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
0.012001
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
0.012001
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
0.012001
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
0.012001
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
YearPublicationVenuePosition
2002 Using Neural Field Dynamics in the Context of Attentional Control
Gerriet Backer, Bärbel Mertsching
ICANN1
2001 Data- and Model-Driven Gaze Control for an Active-Vision System
abstract
Models 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.1