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.
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García-Rodríguez, J., Flórez-Revuelta, F., García-Chamizo, J.M. (2007). Adaptive Representation of Objects Topology Deformations with Growing Neural Gas. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_30
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DOI: https://doi.org/10.1007/978-3-540-73007-1_30
Publisher Name: Springer, Berlin, Heidelberg
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