Motion Illusion-like Patterns Extracted from Photo and Art Images Using Predictive Deep Neural Networks

TL;DR

This study demonstrates that deep neural networks can classify and reproduce motion illusions from static images, revealing insights into visual perception and neural processing of illusions.

Contribution

The paper introduces a neural network approach that detects and generates motion illusion patterns, advancing understanding of visual illusions and neural processing.

Findings

Networks classify illusory images similarly to humans.

Networks detect anomalous motion vectors in static images.

Generated designs evoke illusory motion perception in psychophysical tests.

Abstract

In our previous study, we successfully reproduced the illusory motion of the rotating snakes illusion using deep neural networks incorporating predictive coding theory. In the present study, we further examined the properties of the networks using a set of 1500 images, including ordinary static images of paintings and photographs and images of various types of motion illusions. Results showed that the networks clearly classified illusory images and others and reproduced illusory motions against various types of illusions similar to human perception. Notably, the networks occasionally detected anomalous motion vectors, even in ordinally static images where humans were unable to perceive any illusory motion. Additionally, illusion-like designs with repeating patterns were generated using areas where anomalous vectors were detected, and psychophysical experiments were conducted, in which illusory motion perception in the generated designs was detected. The observed inaccuracy of the networks will provide useful information for further understanding information processing associated with human vision.