Topology conserving mappings for learning motor tasks
AIP Conference Proceedings 151 on Neural Networks for Computing
Topology representing networks
Neural Networks
Self-Organizing Maps
Hand Gesture Recognition Following the Dynamics of a Topology-Preserving Network
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Measuring GNG topology preservation in computer vision applications
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Theoretical analysis of multispectral image segmentation criteria
IEEE Transactions on Image Processing
Hi-index | 0.00 |
Self-organising neural networks try to preserve the topology of an input space by using their competitive learning. This capacity has been used, among others, for the representation of objects and their motion. In this work we use a kind of self-organising network, the Growing Neural Gas, to represent objects as a result of an adaptive process by a topology-preserving graph that constitutes an induced Delaunay triangulation of their shapes. In this paper we present a new hybrid architecture that creates multiple specialized maps to represent different clusters obtained from the multilevel multispectral threshold segmentation.