Recognition of shapes by morphological attributed relational graphs

Skeletons represent a powerful tool for qualitative shape matching because they resume, synthesize and help the understanding both of the object shape and of its topology. The aim of this work is mainly on using the potential strength of skeleton of discrete objects in computer vision and pattern recognition where features of objects are needed for classification. In this paper we propose a method to improve the topological skeleton representation of a binary shape. This allows to find a graph model characterized by efficient and effective attributes, leading to a more correct discrimination between shapes by an attributed graph matching algorithm