BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Development of a Biologically Inspired Real-Time Visual Attention System
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Curious George: An attentive semantic robot
Robotics and Autonomous Systems
Focusing computational visual attention in multi-modal human-robot interaction
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Topological spatial relations for active visual search
Robotics and Autonomous Systems
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Visual search is a common daily human activity and a prerequisite to the interaction with objects encountered in cluttered environments. Humanoid robots that are supposed to take part in human daily life should possess similar capabilities in terms of representing, attending to and recalling objects of interest in order to ensure robust perception in human-centered environments. In this paper, we present necessary processes, memories and representations which allow to identify and store locations of objects, encountered from different angles of view, in a visual search task. In particular, we introduce the so-called Feature Ego-Sphere (FES) as the scene memory for a humanoid robot. Experiments comprising different visual search tasks have been carried out on an active humanoid head equipped with perspective and foveal stereo camera systems. The scene is analyzed actively using both camera systems in order to find instances of searched objects in a consistent and persistent manner.