Characterization and synthesis of objects using growing neural gas

Abstract

In this article it is made a study of the characterization capacity and synthesis of objects of the self-organizing neural models. These networks, by means of their competitive learning, try to preserve the topology of an input space. This capacity is being used for the representation of objects and their movement with topology preserving networks. We characterized the object to represent by means of the obtained maps and kept information solely on the coordinates and the colour from the neurons. From this information it is made the synthesis of the original images, applying mathematical morphology and simple filters on the information which it is had. © Springer-Verlag Berlin Heidelberg 2005.