Self-correcting Symmetry Detection Network

Abstract

In this paper, we propose a symmetry axis detection network that can correct asymmetric parts by itself. Our network compares directional blurring of omnidirectional image edges, which plays a significant role in asymmetry detection and correction. The output layer consists of oscillatory neurons, which activates symmetry axes one by one. Given activated symmetry axis, network estimates the difference of image edges and generates a masking filter to cover the asymmetric parts. The network reconstructs ideal mirror-symmetric image with complete symmetry axes by self-correction. Our network models flexible symmetry perception of high-level cognitive function of human brain.