  We demonstrate the use of similarity domains (SDs) for shape modeling and
skeleton extraction. SDs are recently proposed and they can be utilized in a
neural network framework to help us analyze shapes. SDs are modeled with radial
basis functions with varying shape parameters in Similarity Domains Networks
(SDNs). In this paper, we demonstrate how using SDN can first help us model a
pixel-based image in terms of SDs and then demonstrate how those learned SDs
can be used to extract the skeleton of a shape.
