Informing Computer Vision with Optical Illusions

 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 across multiple scales. A fundamental contribution of this work is to highlight the fact that it may be too early and hastly to reject isotropic filters altogether in favour of orientation selective ones for explaining mid/high level visual phenomena. Therefore the intention here is to link bottom-up processes to higher level perception and cognition consistent with Marr's theory of vision and edge map representation.