Skeleton Pruning Based on the Total Bisector Angle of the End Branches

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Abstract

Skeletonization is an important technique for shape representation, but the skeleton's high sensitivity to boundary noise hampers its application to automatic shape matching. In this paper, we propose a linear-time skeleton pruning algorithm based on the total bisector angle of the end branches. The main idea of the proposed algorithm is to iteratively delete the end branches having the smallest total bisector angle. Since the total bisector angle considers both the local and the global skeleton information, the proposed algorithm removes all redundant branches generated by boundary noise and obtains the ideal skeleton. The experimental results show that the proposed algorithm is highly adaptable, which means that under the same threshold conditions, the pruned skeletons obtained by our proposed algorithm are in accord with human visual perception for most shapes in the MPEG-7 and Tari 56 datasets. Consequently, the pruned skeletons can be applied to automatic shape matching.

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