Multi-scale model-based skeletonization of object shapes using self-organizing maps

A skeletonization algorithm suitable for the skeletonization of sparse shapes is described. It is based on self-organizing maps (SOM)-a class of neural networks with unsupervised learning. The so-called structured SOM with local shape attributes such as scale and connectivity of vertices are used to determine the object shape in the form of piecewise linear skeletons. The location of each vertex of piecewise linear generating lines on the image plane corresponds to the position of a particular SOM unit. This method makes it possible to extract the object skeletons and to reconstruct the planar shape of sparse objects based on the topological constraints of generating lines and estimation of scales.