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

CVPRW 2019 pp. 1138-1142

doi:10.1109/CVPRW.2019.00148 /cvprw/2019/ozer2019cvprw-parametric/

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.

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Cite

Text

Ozer. "Parametric Shape Modeling and Skeleton Extraction with Radial Basis Functions Using Similarity Domains Network." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00148

Markdown

[Ozer. "Parametric Shape Modeling and Skeleton Extraction with Radial Basis Functions Using Similarity Domains Network." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/ozer2019cvprw-parametric/) doi:10.1109/CVPRW.2019.00148

BibTeX

@inproceedings{ozer2019cvprw-parametric,
  title     = {{Parametric Shape Modeling and Skeleton Extraction with Radial Basis Functions Using Similarity Domains Network}},
  author    = {Ozer, Sedat},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2019},
  pages     = {1138-1142},
  doi       = {10.1109/CVPRW.2019.00148},
  url       = {https://mlanthology.org/cvprw/2019/ozer2019cvprw-parametric/}
}