<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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 <italic>k</italic> 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.</p></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.

Curve Skeleton Extraction Via <italic>K</italic>–Nearest–Neighbors Based Contraction

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Curve Skeleton Extraction Via –Nearest–Neighbors Based Contraction

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...

Curve Skeleton Extraction Via K–Nearest–Neighbors Based Contraction

Publié en ligne: 03 avr. 2020

Pages: 123 - 132

Reçu: 13 mai 2019

Accepté: 18 oct. 2019

DOI: https://doi.org/10.34768/amcs-2020-0010

Mots clés
curve skeleton, points contraction, point cloud, nearest neighbors

© 2020 Jianling Zhou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Curve Skeleton Extraction Via K–Nearest–Neighbors Based Contraction

Publié en ligne: 03 avr. 2020

Pages: 123 - 132

Reçu: 13 mai 2019

Accepté: 18 oct. 2019

DOI: https://doi.org/10.34768/amcs-2020-0010

Mots cléscurve skeleton, points contraction, point cloud, nearest neighbors

© 2020 Jianling Zhou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
curve skeleton, points contraction, point cloud, nearest neighbors