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

Autor:	Koen Ponse, Anna V. Kononova, Maria Loleyt, Bas Van Stein
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition TheoryofComputation_GENERAL Machine Learning (cs.LG)
DOI:	10.48550/arxiv.2201.06594
Popis:	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. Comment: 8 pages, 12 figures
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13c0cc515d721547807da2f40dce7bdc Zobrazit plný text záznamu