An improved method for CNN-based detection of symmetry axis in black and white images

In this paper, a method for symmetry axis detection in binary images is presented. The method is an improvement of a previous method presented by the same authors. 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 many real images, with good results.