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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/49278
Title: A novel fully parallel skeletonization algorithm
Authors: Jun Ma
Xunhuan Ren
Tsviatkou, V. Y.
Kanapelka, V. K.
Keywords: публикации ученых;Skeletonization;Noise immunity;Fully parallel
Issue Date: 2022
Publisher: Springer
Citation: A novel fully parallel skeletonization algorithm / Jun Ma1 [and others] // Pattern Analysis and Applications. – 2022. – Vol. 25. – 169–188. – DOI : 10.1007/s10044- 021-01039-y.
Abstract: A Skeleton that is extracted by a skeletonization algorithm from a binary image is useful for object description, matching, recognition and compression. The parallel thinning algorithm, one of the skeletonization algorithms is well known to have computational effciency. The main contribution of this paper is that we proposed a novel fully parallel thinning algorithm based on a comprehensive investigation of the well-known Zhang-Suen (ZS)-series algorithms and the one-pass thinning algorithm (OPTA)-series algorithms, which not only has good performance in terms of (8,4) connectivity preservation and single-pixel thickness, but also has the following qualities: it is more robust to the boundary noise than the OPTA-series algorithms and it is faster than the ZS-series algorithms in terms of thinning speed, as confirmed by the experiments presented in this paper.
URI: https://libeldoc.bsuir.by/handle/123456789/49278
Appears in Collections:Публикации в зарубежных изданиях

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