Abstract
An image thinning technique using a neural network is proposed. Using different activation functions at different layers, the proposed neural network removes the boundary pixels from four directions in such a manner that the general configuration of the input pattern is unaltered and the connectivity is preserved. The resulting object, called a skeleton, provides an abstraction of the global shape of the object. The skeleton is often useful for geometrical and structural analysis of the object. The output skeleton here satisfies the basic properties of a skeleton, namely connectivity and unit thickness. The proposed method is experimentally found to be more efficient in terms of better medial axis representation and robustness to boundary noise over a few existing algorithms.
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