Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images

TL;DR

This paper enhances automated symmetry detection in 2D images by improving reflection symmetry algorithms and introducing a machine learning model to identify rotational symmetries, aiding applications in art analysis and computer vision.

Contribution

It proposes post-processing improvements for reflection symmetry detection and introduces a machine learning approach for rotational symmetry classification.

Findings

Improved localised symmetry detection in images.

Effective machine learning model for rotational symmetry classification.

Enhanced understanding of symmetry types in 2D images.

Abstract

Automated symmetry detection is still a difficult task in 2021. However, it has applications in computer vision, and it also plays an important part in understanding art. This paper focuses on aiding the latter by comparing different state-of-the-art automated symmetry detection algorithms. For one of such algorithms aimed at reflectional symmetries, we propose post-processing improvements to find localised symmetries in images, improve the selection of detected symmetries and identify another symmetry type (rotational). In order to detect rotational symmetries, we contribute a machine learning model which detects rotational symmetries based on provided reflection symmetry axis pairs. We demonstrate and analyze the performance of the extended algorithm to detect localised symmetries and the machine learning model to classify rotational symmetries.