Extracting symmetry axes: a neural network model

This paper proposes a neural network model that extracts axes of symmetry from visual patterns. The input patterns can be line drawings, plane figures or gray-scaled natural images taken by CCD cameras. The model is a multi-layered network. It has an input layer, a contrast-extracting layer, edge-extracting layers (an S-cell layer and a C-cell layer), and layers extracting symmetry axes. These layers are connected in a cascade in a hierarchical manner. The model extracts oriented edges from the input image first, and then tries to extract axes of symmetry. To reduce the computational cost, the model checks conditions of symmetry, not directly from the oriented edges, but from a blurred version (low-resolution responses covering a large area) of them. The use of blurred signals endows the network with a large tolerance to deformation of input patterns, too.