On the Generation of Skeletons from Discrete Euclidean Distance Maps

The skeleton is an important representation for shape analysis. A common approach for generating discrete skeletons takes three steps: 1) computing the distance map, 2) detecting maximal disks from the distance map, and 3) linking the centers of maximal disks (CMDs) into a connected skeleton. Algorithms using approximate distance metrics are abundant and their theory has been well established. However, the resulting skeletons may be inaccurate and sensitive to rotation. In this paper, we study methods for generating skeletons based on the exact Euclidean metric. We first show that no previous algorithms identify the exact set of discrete maximal disks under the Euclidean metric. We then propose new algorithms and show that they are correct. To link CMDs into connected skeletons, we examine two prevalent approaches: connected thinning and steepest ascent. We point out that the connected thinning approach does not work properly for Euclidean distance maps. Only the steepest ascent algorithm produces skeletons that are truly medially placed. The resulting skeletons have all the desirable properties: they have the same simple connectivity as the figure, they are well-centered, they are insensitive to rotation, and they allow exact reconstruction. The effectiveness of our algorithms is demonstrated with numerous examples.