Detection of Symmetry Axis by a CNN-based Algorithm

In this paper a method for symmetry axis detection in binary images is presented. The method exploits the nonlinear dynamic behavior of Cellular Neural Networks (CNNs), in particular the propagation of bipolar waves. The image is represented in polar form, transforming the symmetry with respect to an arbitrarily oriented axis in a vertical symmetry: the position of the vertical axis corresponds to the angle of the original symmetry axis. The parallel CNN architecture is useful to speed up the computation, because of the high computational cost of the task. The proposed algorithm is tested on a real image with good results.