A CONNECTIONIST APPROACH FOR VISUAL PERCEPTION OF MOTION

Abstract

Modeling visual perception of motion by connectionist networks offers various areas of research for the development of real-time models of dynamic perception-action. In this paper we present the bases of a bio-inspired connectionist approach that is part of our development of neural networks applied to autonomous robotics. Our model of visual perception of motion is based on a causal adaptation of spatiotemporal Gabor lters. We use our causal spatiotemporal lters within a modular and strongly localized architecture that performs a shunting inhibition mechanism. This model has been evaluated on articial as well as natural image sequences.