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Curve Skeleton Extraction Via K–Nearest–Neighbors Based Contraction Cover

Curve Skeleton Extraction Via K–Nearest–Neighbors Based Contraction

By: Jianling Zhou,  Ji Liu and  Min Zhang  
Open Access
|Apr 2020

Abstract

We propose a skeletonization algorithm that is based on an iterative points contraction. We make an observation that the local center that is obtained via optimizing the sum of the distance to k nearest neighbors possesses good properties of robustness to noise and incomplete data. Based on such an observation, we devise a skeletonization algorithm that mainly consists of two stages: points contraction and skeleton nodes connection. Extensive experiments show that our method can work on raw scans of real-world objects and exhibits better robustness than the previous results in terms of extracting topology-preserving curve skeletons.

DOI: https://doi.org/10.34768/amcs-2020-0010 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 123 - 132
Submitted on: May 13, 2019
Accepted on: Oct 18, 2019
Published on: Apr 3, 2020
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2020 Jianling Zhou, Ji Liu, Min Zhang, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.