Structural and Textural Skeletons for Noisy Shapes

Abstract

The extraction of consistent skeletons in the presence of boundary noise is still a problem for most skeletonization algorithms. Many suppress skeletons associated with boundary perturbation, either by preventing their formation or removing them subsequently using additional operations. A more appropriate approach is to view a shape as comprising of structural and textural skeletons. The former describes the general structure of the shape and the latter its boundary characteristics. These two types of skeletons should be encouraged to remaining disconnected to facilitate gross shape matching without the need for branch pruning. Such skeletons can be formed by means of a multi-resolution gradient vector field (MGVF), which can be generated efficiently using a pyramidal framework. The robust scale-invariant extraction of the skeletons from the MGVF is described. Experimental results show that the MGVF structural skeletons are less affected by boundary noise compared to skeletons extract by other popular iterative and non-iterative techniques.