Visual shape recognition with contour propagation

Abstract

A neural architecture is presented that encodes the visual space inside and outside of a shape. The contours of a shape are propagated across an excitable neuronal map and fed through a set of orientation columns, thus creating a pattern which can be viewed as a vector field. This vector field is then burned as synaptic, directional connections into a propagation map, which will serve as a "shape map". The shape map identifies its own, preferred input when it is translated, deformed, scaled and fragmented, and discriminates other shapes very distinctively. Encoding visual space is much more efficient for shape recognition than determining contour geometry only.