A skeleton pruning algorithm based on information fusion
Pattern Recognition Letters
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We present a skeleton computation algorithm for binary image shape which is stable and efficient. The algorithm follows these steps: first the shape boundary curves are subsampled, then the Voronoi Skeleton is computed from the resulting reduced boundary set of points, and finally, a novel two stage pruning procedure is applied to obtain a simplified skeleton. The first stage removes skeleton edges non fully included in the shape. The second stage applies an enhanced variation of the Discrete Curve Evolution (DCE) for Voronoi skeletons. We obtain improved skeleton stability, complexity reduction and noise robustness. Pruning computing time efficiency is improved thanks to some properties of Voronoi skeletons. Entire skeleton edges can be removed or retained on the basis of conditions tested on the edge endpoints. Pattern recognition experiments and skeleton stability experiments of the algorithm outperform previous approaches in the literature.