A Novel Hierarchical Shape Analysis based on Sampling Point-Line Distance for Regular and Symmetry Shape Detection

Regular and symmetry shapes occurred in natural and manufactured objects. Detecting these shapes are essential and still tricky task in computer vision. This paper proposes a novel hierarchical shape detection (HiSD) method, which consists of circularity and roundness detection, and regularity and symmetry detection phases. The first phase recognizes the circular and elliptical shapes using aspect ratio and roundness measurements. The second phase, the main phase in the HiSD, recognizes the regular and symmetry shapes using density distribution measurement (DDM) and the proposed sampling point-line distance distribution (SPLDD) algorithm. The proposed method presets effective with low computation cost shape detection approach which is not sensitive to specific category of objects. It enables to detect different types of objects involving the arbitrary, regular, and symmetry shapes. Experimental results show that the proposed method performs well compared to the existing state-of-the-art algorithms.

@article{Xian2022,
  title     = {A Novel Hierarchical Shape Analysis based on Sampling Point-Line Distance for Regular and Symmetry Shape Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {11},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Kehua Xian},
  doi       = {10.14569/IJACSA.2022.0131159},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131159}
}