Dynamic Neural Field Theory for Motion Perception
Dynamic Neural Field Theory for Motion Perception
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Competitive-Layer Model for Feature Binding and Sensory Segmentation
Neural Computation
Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Cortical Mechanism for Binding in Visual Working Memory
Journal of Cognitive Neuroscience
Dynamic field theory and embodied communication
ZiF'06 Proceedings of the Embodied communication in humans and machines, 2nd ZiF research group international conference on Modeling communication with robots and virtual humans
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A new clustering approach on the basis of dynamical neural field
Neural Computation
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Autonomous robots interacting with human users need to build and continuously update scene representations. This entails the problem of rapidly learning to recognize new objects under user guidance. Based on analogies with human visual working memory, we propose a dynamical field architecture, in which localized peaks of activation represent objects over a small number of simple feature dimensions. Learning consists of laying down memory traces of such peaks. We implement the dynamical field model on a service robot and demonstrate how it learns 30 objects from a very small number of views (about 5 per object are sufficient). We also illustrate how properties of feature binding emerge from this framework.