Skeletonization by a topology-adaptive self-organizing neural network

摘要

A self-organizing neural network model is proposed to generate the skeleton of a pattern. The proposed neural net is topology-adaptive and has a few advantages over other self-organizing models. The model is dynamic in the sense that it grows in size over time. The model is especially designed to produce a vector skeleton of a pattern. It works on binary patterns, dot patterns and also on gray-level patterns. Thus it provides a unified approach to skeletonization. The proposed model is highly robust to noise (boundary and interior noise) as compared to existing conventional skeletonization algorithms and is invariant under arbitrary rotation. It is also efficient in medial axis representation and in data reduction.