Robust skeletonization using the discrete λ-medial axis

Medial axes and skeletons are notoriously sensitive to contour irregularities. This lack of stability is a serious problem for applications in e.g. shape analysis and recognition. In 2005, Chazal and Lieutier introduced the lambda-medial axis as a new concept for computing the medial axis of a shape subject to single parameter filtering. The lambda-medial axis is stable under small shape perturbations, as proved by these authors. In this article, a discrete lambda-medial axis (DLMA) is introduced and compared with the recently introduced integer medial axis (GIMA). We show that DLMA provides measurably better results than GIMA, with regard to stability and sensibility to rotations. We give efficient algorithms to compute the DLMA, and we also introduce a variant of the DLMA which may be computed in linear-time. (C) 2010 Elsevier B.V. All rights reserved.