Recognition of Similar Patterns by Multilayer Nets and Detection of Rotated Angle and Scale Ratio

Abstract

For practical pattern recognition, it is required not only to recognize geometrically similar patterns but also to detect the difference of translation, rotation and size from their templates. This paper proposes a method to recognize the similar patterns by a multilayer net and then detect the difference on the common basis of well-known geometrical characteristics (center of gravity, angle of principal axis, and variance). It is found from experimental results that, with the proposed method, a small net can classify the similar patterns at a high recognition rate and detect their rotated angles and scale ratios with a high accuracy.