Abstract
Real images contain reflection symmetry and repetition in rows with high probability. I.e. certain parts can be mapped on other certain parts by the usual Gestalt laws and are repeated there with high similarity. Moreover, such mapping comes in nested hierarchies – e.g. a reflection Gestalt that is made of repetition friezes, whose parts are again reflection symmetric compositions. It is our intention to develop and test methods that may automatically find, parametrize, and assess such nested hierarchies. This can be explicitly modelled by continuous assessment functions. The recognition performance is raised utilizing additional features such as colors. This paper reports examples from the 2017 data set.