Open Access

ARTICLE

Effective Piecewise Linear Skeletonization of Sparse Shapes

Wenyu Qu¹, Zhiyang Li^2,*, Junfeng Wu², Yinan Wu³, Zhaobin Liu²

1 School of Computer Software, Tianjin University, China
2 School of Information Science and Technology, Dalian Maritime University, China
3 87 Unit, 91550 of PLA, Dalian, China

* Corresponding Author: E-mail:

Computer Systems Science and Engineering 2018, 33(2), 115-123. https://doi.org/10.32604/csse.2018.33.115

Abstract

Conventional image skeletonization techniques implicitly assume the pixel level connectivity. However, noise inside the object regions destroys the connectivity and exhibits sparseness in the image. We present a skeletonization algorithm designed for these kinds of sparse shapes. The skeletons are produced quickly by using three operations. First, initial skeleton nodes are selected by farthest point sampling with circles containing the maximum effective information. A skeleton graph of these nodes is imposed via inheriting the neighborhood of their associated pixels, followed by an edge collapse operation. Then a skeleton tting process based on feature-preserving Laplacian smoothing is applied. Finally, a re nement step is proposed to further improve the quality of the skeleton and deal with noise or different local shape scales. Numerous experiments demonstrate that our algorithm can effectively handle several disconnected shapes in an image simultaneously, and generate more faithful skeletons for shapes with intersections or different local scales than classic methods.

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Keywords

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