Computational aspects of motion perception in natural and artificial vision systems

Abstract

In this paper a computational scheme for motion perception in artificial and natural vision systems is described. The scheme is motivated by a mathematical analysis in which first-order spatial properties of optical flow, such as singular points and elementary components of optical flow, are shown to be salient features for the computation and analysis of visual motion. The fact that different methods for the computation of optical flow produce similar results is explained in terms of the simple spatial structure of the image motion of rigid bodies. Singular points and elementary flow components are used to compute motion parameters, such as time-to-collision and angular velocity, and also to segment the visual field into areas which correspond to different motions. Then a number of biological implications are discussed. Electrophysiological findings suggest that the brain perceives visual motion by detecting and analysing optical flow components. However, the cortical neurons, which seem to detect elementary flow components, are not able to extract these components from more complex flows. A simple model for the organization of the receptive field of these cells, which is consistent with anatomical and electrophysiological data, is described at the end of the paper.