# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1902.02922