<title>Physiological model of motion analysis for machine vision</title>

Abstract

We studied the spatio-temporal shape of `receptive fields' of simple cells in the monkey visual cortex. Receptive fields are maps of the regions in space and time that affect a cell's electrical responses. Fields with no change in shape over time responded to all directions of motion; fields with changing shape over time responded to only some directions of motion. A Gaussian Derivative (GD) model fit these fields well, in a transformed variable space that aligned the centers and principal axes of the field and model in space-time. The model accounts for fields that vary in orientation, location, spatial scale, motion properties, and number of lobes. The model requires only ten parameters (the minimum possible) to describe fields in two dimensions of space and one of time. A difference-of-offset-Gaussians (DOOG) provides a plausible physiological means to form GD model fields. Because of its simplicity, the GD model improves the efficiency of machine vision systems for analyzing motion. An implementation produced robust local estimates of the direction and speed of moving objects in real scenes.