Name
Abstract | ||
|---|---|---|
Self-organising neural networks try to preserve the topology of an input space by means of 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 deformations in objects along a sequence of images. As a result of an adaptive process the objects are represented by a topology representing graph that constitutes an induced Delaunay triangulation of their shapes. These maps adapt the changes in the objects topology without reset the learning process. |
Year | Venue | Keywords |
|---|---|---|
2007 | IWANN | self-organising network,objects topology,adaptive representation,input space,neural network,induced delaunay triangulation,objects topology deformation,neural gas,competitive learning,adaptive process |
Field | DocType | Volume |
Competitive learning,Computer science,Artificial intelligence,Artificial neural network,Adaptive representation,Delaunay triangulation,Digital topology,Computer vision,Topology,Graph,Neural gas,Machine learning,Quad-edge | Conference | 4507 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José García Rodríguez | 1 | 192 | 29.10 |
| Francisco Flórez-revuelta | 2 | 481 | 34.95 |
| Juan Manuel García-Chamizo | 3 | 72 | 8.98 |