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

• URL: http://arxiv.org/abs/2201.06594v1
• Date: Mon, 17 Jan 2022 19:14:58 GMT
• Title: Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images
• Authors: Koen Ponse, Anna V. Kononova, Maria Loleyt, Bas van Stein
• Abstract summary: This paper focuses on aiding the latter by comparing different state-of-the-art automated symmetry detection algorithms. We propose post-processing improvements to find localised symmetries in images, improve the selection of detected symmetries and identify another symmetry type (rotational) We demonstrate and analyze the performance of the extended algorithm to detect localised symmetries and the machine learning model to classify rotational symmetries.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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