基于边界曲线演化模型的生长骨架算法

A Skeleton-growing Algorithm Based on Boundary Curve Evolution

基于距离变换的骨架算法往往不能直接用于骨架识别,且骨架的连通性难以保证．本文提出一种新型的骨架算法,由一个初始骨架点开始逐点生长出各骨架分支,同时在骨架生长过程中用离散曲线演化模型消除造成信息冗余的骨架枝,保留视觉上重要的骨架枝,实现了骨架的多尺度控制．实验证明本算法复杂度低,得到的骨架连通性得到保证,能较好地表示图形中视觉重要成分,符合人类视觉习惯,可直接用于图形识别和形状度量．

Traditional skeletonization algorithm based on the distance transform can not be used for skeleton recognition directly, and the connectivity property of the skeleton is not guaranteed. A novel skeletonization algorithm is presented, in which the whole skeleton is obtained by growing from the original skeleton seed one by one. In the growing process, the redundant skeleton branches are eliminated by the discrete curve evolution model, the visual branches remain completely, and the hierarchical control can be achieved easily. Examples have showed that the complexity of this algorithm is low, the connectivity of the skeleton is guaranteed, and the skeleton can represent the visual parts to satisfy human vision. The algorithm can be used in graphic recognition and shape measurement.