Abstract
Some modifications on Kohonen's self-organizing feature map are discussed to make it suitable for finding skeletons of binary images. In Kohonen's feature map, the set of processors and their neighbourhoods are fixed and do not change in the learning process. This may pose problems when the set of input vectors represents a prominent shape. The reference vectors or weight vectors lying in zero-density areas are affected by input vectors from all the surrounding parts of the non-zero distribution [5]. Hence a shape extraction problem requires a dynamic change in the network topology. In the present paper, to overcome the limitations of Kohonen's feature maps, we propose a mechanism in which the set of processors and their neighbourhoods change adaptively during learning, to extract the shape of a binary object in the form of a skeleton.
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