Skeletonisation: An electrostatic field-based approach

Abstract

Skeleton representation of an object is a powerful shape descriptor that captures both boundary and region information of the object. The skeleton of a shape is a representation composed of idealized thin lines that preserve the connectivity or topology of the original shape. Although the literature contains a large number of skeletonisation algorithms, many open problems remain. In this paper, we present a new skeletonisation approach that relies on the Electrostatic Field Theory (EFT). Many problems associated with existing skeletonisation algorithms are solved using the proposed approach. In particular, connectivity, thinness and other desirable features of a skeleton are guaranteed. It also captures notions of corner detection, multiple scale, thinning, and skeletonisation all within one unified framework. The performance of the proposed EFT-based algorithm is studied extensively. Using the Hausdorf distance measure, the noise sensitivity of the algorithm is compared to two existing skeletonisation techniques. In addition, the experimental results also demonstrate the multiscale property of the proposed approach.