Neural gradient models for the measurement of image velocity

Although gradient schemes for detecting the motion of images and measuring their velocities are commonly used in computer vision, and although there is increasing evidence to support the existence of such schemes in biological vision, little attention has been directed to suggesting how such computations might be realized by neural hardware. This paper proposes two simple models, consisting of physiologically realistic networks of neurons, that approximate the gradient scheme. Computer simulations demonstrate that the models measure the speed of an object or pattern independently of its structural properties.