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

IEEE Symposium Series on Computational Intelligence (SSCI), pp. 01–08

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 postprocessing 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.

Authors 4

1. Koen Ponse Aachen

   Leiden University

   Affiliation as printed

   LIACS, Leiden University, Leiden, The Netherlands

2. Anna V. Kononova Aachen

   Leiden University

   Affiliation as printed

   LIACS, Leiden University, Leiden, The Netherlands

3. Maria Loleyt
   Affiliation as printed

   Ornamika, Moscow, Russia

4. Bas van Stein Aachen

   Leiden University

   Affiliation as printed

   LIACS, Leiden University, Leiden, The Netherlands