  Illusions are fascinating and immediately catch people's attention and
interest, but they are also valuable in terms of giving us insights into human
cognition and perception. A good theory of human perception should be able to
explain the illusion, and a correct theory will actually give quantifiable
results. We investigate here the efficiency of a computational filtering model
utilised for modelling the lateral inhibition of retinal ganglion cells and
their responses to a range of Geometric Illusions using isotropic Differences
of Gaussian filters. This study explores the way in which illusions have been
explained and shows how a simple standard model of vision based on classical
receptive fields can predict the existence of these illusions as well as the
degree of effect. A fundamental contribution of this work is to link bottom-up
processes to higher level perception and cognition consistent with Marr's
theory of vision and edge map representation.
