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Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution

Bai, Xiang, Latecki, Longin, Liu, Wen-yu·2007·IEEE Transactions on Pattern Analysis and Machine Intelligence

Abstract

In this paper, we introduce a new skeleton pruning method based on contour partitioning. Any contour partition can be used, but the partitions obtained by Discrete Curve Evolution (DCE) yield excellent results. The theoretical properties and the experiments presented demonstrate that obtained skeletons are in accord with human visual perception and stable, even in the presence of significant noise and shape variations, and have the same topology as the original skeletons. In particular, we have proven that the proposed approach never produces spurious branches, which are common when using the known skeleton pruning methods. Moreover, the proposed pruning method does not displace the skeleton points. Consequently, all skeleton points are centers of maximal disks. Again, many existing methods displace skeleton points in order to produces pruned skeletons.

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Metadata

Authors
Bai, X., Latecki, L., & Liu, W.-Y.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
2007-03
Volume
29
Issue
3
Pages
449-462
ISSN
0162-8828 (ISSN-L)
OpenAlex
W2104093257
Cited by
425
References
0
FWCI
35.35
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution. https://app.cochat.ai/papers/10.1109/tpami.2007.59
The paper
Bai, X., Latecki, L., & Liu, W.-Y. (2007). Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2007.59

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.