# Parametric Shape Modeling and Skeleton Extraction with Radial Basis Functions using Similarity Domains Network

## TL;DR

This paper introduces a novel approach using Similarity Domains Networks with radial basis functions for shape modeling and skeleton extraction from images, demonstrating the effectiveness of SDs in neural network frameworks.

## Contribution

It presents a new method combining SDs and RBFs within neural networks for shape analysis and skeleton extraction, advancing shape modeling techniques.

## Key findings

- SDNs effectively model pixel-based images with SDs
- Learned SDs can accurately extract shape skeletons
- The approach enhances shape analysis with neural networks

## Abstract

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.