Extracting curve-skeletons from digital shapes using occluding contours

Curve-skeletons are compact and semantically relevant shape descriptors, able to summarise both topology and pose of a wide range of digital objects. Most of the state-of-the-art algorithms for their computation rely on the type of geometric primitives used and sampling frequency. In this paper, we introduce a formally sound and intuitive definition of a curve-skeleton, then we propose a novel method for skeleton extraction that relies on the visual appearance of the shapes. To achieve this result, we inspect the properties of occluding contours, showing how information about the symmetry axes of a 3D shape can be inferred from a small set of its planar projections. The proposed method is fast, insensitive to noise and resolution, capable of working with different shape representations, and easy to implement.