Use of non-uniform spatial blur for image comparison: symmetry axis extraction

This paper shows that the introduction of non-uniform blur is very useful for comparing images, and proposes a neural network model that extracts axes of symmetry from visual patterns. The blurring operation greatly increases robustness against deformations and various kinds of noise, and largely reduces computational cost. Asymmetry between two groups of signals can be detected in a single action by the use of non-uniform blur having a cone-shaped distribution. The proposed model is a hierarchical multi-layered network, which consists of a contrast-extracting layer, edge-extracting layers (simple and complex types), and layers extracting symmetry axes. The model extracts oriented edges from an input image first, and then tries to extract axes of symmetry. The model checks conditions of symmetry, not directly from the oriented edges, but from a blurred version of the response of edge-extracting layer. The input patterns can be complicated line drawings, plane figures or gray-scaled natural images taken by CCD cameras.