Implementation of parallel thinning algorithms using recurrent neural networks

The use of recurrent neural networks for skeletonization and thinning of binary images is investigated. The networks are trained to learn a deletion rule and they iteratively delete object pixels until only the skeleton remains. Recurrent neural network architectures that implement a variety of thinning algorithms, such as the Rosenfeld-Kak algorithm and the Wang-Zhang (WZ) algorithm, are presented. A modified WZ algorithm which produces skeletons that are intuitively more pleasing is introduced.<>