Abstract
Skeletonization is a necessary process in a variety of applications in image processing and object recognition. However, the concept of a skeleton, defined using either the union of centers of maximal discs or the union of points with more than one generating points, was originally formulated in continuous space. When they are applied to situation in discrete space, the resulting skeletons may become disconnected and further works are needed to link them.In this paper, we propose a novel skeletonization method which extends the concept of a skeleton to include both continuous and discrete space using generalized Voronoi diagrams. We also present a skeleton pruning method which is able to remove noisy branches by evaluating their significance. Three experimental results demonstrate that: (1) our method is stable across a wide range of shapes, and (2) it performs better in accuracy and robustness than previous approaches for processing shapes whose boundaries contain substantial noise.
Published Version
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