  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.
