title: A geometric framework for nonlinear visual coding.
abstract: It is argued that important aspects of early and middle level visual coding may be understood as resulting from basic geometric processing of the visual input. The input is treated as a hypersurface defined by image intensity as a function of two spatial coordinates and time. Analytical results show how the Riemann curvature tensor R of this hypersurface represents speed and direction of motion. Moreover, the results can predict the selectivity of MT neurons for multiple motions and for motion in a direction along the optimal spatial orientation. Finally, a model based on integrated R components predicts global-motion percepts related to the barber-pole illusion.
authors.forename: E
authors.initials: E
authors.lastname: Barth
authors.forename: A
authors.initials: A
authors.lastname: Watson