Multiple rotation symmetry group detection via saliency-based visual attention and Frieze expansion pattern

Abstract

Global maximum symmetry center probability-based rotation symmetry detection methods are unable to identify small-scale rotation symmetry centers in real-world images and come with a costly computational burden. This paper presents a novel strategy comprised of multiple-local-region searching in the whole image and multiple-model computation to map rotation symmetry strength (RSS) in the local region. The multiple-local-region searching method creates local regions in which the global maximum symmetry center detection method changes to a local maximum symmetry center detection method. In the local region, the multiple-model computation efficiently detects regular, small-scale, and skewed symmetry centers. This strategy improves the detection ability for regular, small-scale, and skewed rotation symmetry centers while minimizing the complexity of the algorithm based on a rotation symmetry strength (RSS) map and symmetry shape density (SSD) map. Experimental results indicate that this strategy not only allows more rotation symmetry centers to be identified successfully, but is simpler than traditional strategies as it only employs RSS maps.