- Poster presentation
- Open Access
Multi-modal novelty and familiarity detection
© Panchev; licensee BioMed Central Ltd. 2013
- Published: 8 July 2013
- Mobile Robot
- Spatial Attention
- Motor Modality
- Perceptual Modality
- Visual Module
Presented is further development of the architecture presented in  where a top-down feature-based and spatial attention have been incorporated in a large scale visual module and novelty and familiarity detectors based on the model presented in . These have been developed in the perceptual (visual and auditory) and motor modalities. In addition to the novelty/familiarity detection shown in [2, 3], the architecture is able to partially recognise familiar features in each perceptual modality, and furthermore in a distributed fashion activate associated familiar features from one perceptual modality to another and/or to the motor programmes and affordances. The architecture is implemented on a mobile robot operating in a dynamic environment. The proposed distributed multi-modal familiarity detection integrated in the architecture improves the recognition and action performance in a noisy environment, as well as contributing to the multi-modal association and learning of novel objects and actions.
- Panchev C: An Oscillatory Model for Multimodal Processing of Short Language Instructions. Proceedings of the International Conference on Artificial Neural Networks (ICANN). 2007, 4669: 425-434.Google Scholar
- Taylor N, Taylor JG: A Novel Novelty Detector. Proceedings of the International Conference on Artificial Neural Networks (ICANN). 2007, 4669: 973-983.Google Scholar
- Taylor NR, Panchev C, Hartley M, Kasderidis S, Taylor JG: Occlusion, Attention and Object Representations. ICANN. 2006, 1: 592-601.Google Scholar
- Panchev C: Computing with active dendrites. Neurocomputing. 2007, 70 (10-12): 1702-1705. 10.1016/j.neucom.2006.11.002.View ArticleGoogle Scholar
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