Neural Competitive Structures for Segmentation Based on Motion Features

Simoncelli & Heeger studied how the motion is processed in humans (V1 and MT areas) and proposed a model based on neural populations that extract the local motion structure through local competition of MT like cells. In this paper we present a neural structure that works as dynamic filter on the top of this MT layer and can take advantage of the neural population coding that is supposed to be present in the MT cortical processing areas. The test bed application addressed in this work is an automatic watch up system for the rear-view mirror blind spot. The segmentation of overtaking cars in this scenario can take full advantage of the motion structure of the visual field provided that the egomotion of the host car induces a global motion pattern whereas an overtaking car produces a motion pattern highly contrasted with this global ego-motion field.