Neural Network Model for Detection of Edges Defined by Image Dynamics

Insects can detect the presence of discrete objects in their visual fields based on a range of differences in spatiotemporal characteristics between the images of object and background. This includes but is not limited to relative motion. Evidence suggests that edge detection is an integral part of this capability, and this study examines the ability of a bio-inspired processing model to detect the presence of boundaries between two regions of a one-dimensional visual field, based on general differences in image dynamics. The model consists of two parts. The first is an early vision module inspired by insect visual processing, which implements adaptive photoreception, ON and OFF channels with transient and sustained characteristics, and delayed and undelayed signal paths. This is replicated for a number of photoreceptors in a small linear array. It is followed by an artificial neural network trained to discriminate the presence vs. absence of an edge based on the array output signals. Input data are derived from natural imagery and feature both static and moving edges between regions with moving texture, flickering texture, and static patterns in all possible combinations. The model can discriminate the presence of edges, stationary or moving, at rates far higher than chance. The resources required (numbers of neurons and visual signals) are realistic relative to those available in the insect second optic ganglion, where the bulk of such processing would be likely to take place.