Informing Computer Vision with Optical Illusions

TL;DR

This paper demonstrates that a simple computational model based on classical receptive fields can predict and explain various geometric optical illusions, linking low-level visual processing to higher-level perception.

Contribution

It introduces a straightforward filtering model that effectively predicts illusion effects, bridging bottom-up retinal processes with higher-level visual cognition.

Findings

Model predicts the existence of geometric illusions

Model quantifies the degree of illusion effects

Links retinal inhibition to perception of illusions

Abstract

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.