A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
A Fast Simplified Fuzzy ARTMAP Network
Neural Processing Letters
Foreground object detection from videos containing complex background
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Crossmodal content binding in information-processing architectures
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
A hierarchical classifier with growing neural gas clustering
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
TopoART: a topology learning hierarchical ART network
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
An extended TopoART network for the stable on-line learning of regression functions
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
IEEE Transactions on Neural Networks
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Robotic application scenarios in uncontrolled environments pose high demands on mobile robots. This is especially true if human-robot interaction or robot-robot interaction is involved. Here, potential interaction partners need to be identified. To tackle challenges like this, robots make use of different sensory systems. In many cases, these robots have to deal with erroneous data from different sensory systems which often are processed separately. A possible strategy to improve identification results is to combine different processing results of complementary sensors. Their relation is often hard coded and difficult to learn incrementally if new kinds of objects or events occur. In this paper, we present a new fusion strategy which we call the Simplified Fusion ARTMAP (SiFuAM) which is very flexible and therefore can be easily adapted to new domains or sensor configurations. As our approach is based on the Adaptive Resonance Theory (ART) it is inherently capable of incremental on-line learning. We show its applicability in different robotic scenarios and platforms and give an overview of its performance.