Parametric Shape Modeling and Skeleton Extraction with Radial Basis Functions using Similarity Domains Network
This work addresses shape analysis problems in computer vision, but it appears incremental as it builds on recently proposed similarity domains.
The paper tackles shape modeling and skeleton extraction by using similarity domains (SDs) modeled with radial basis functions in a neural network framework, demonstrating how SDs can represent pixel-based images and extract shape skeletons.
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.