LAUSR

Cultural Heritage (CH) artifacts generally possess symmetry of reflection, rotation, translation and glide reflection in their shape. Similarity measures are used to determine complex 3D models where symmetry is considered to be one of the similarity signatures. This work presents the methodology of detecting symmetry in 3D objects based on the three techniques: (1) Eigenvalues and Eigenvectors, (2) local surface discontinuity, and (3) pixel orientation. In this work, these methods have been modified suitably for the detection of symmetry in CH artifacts. Among these methods, it is found that the first two methods yield better performance on the symmetry signature estimation of 98 percent for complex models and up to 100% for primitive models. The execution time of the proposed methods is compared with the state-of-the-art approaches available in the literature. Three levels of random 3D models available in the internet repository are analyzed for efficiency, performance and robustness. At each level, the accuracy of the Eigenvalue method and the local discontinuity method is found to be better than the pixel orientation method. The modified algorithms have been tested for better performance with F-score, robustness, and execution time with 3D benchmark dataset and cultural heritage dataset available in the literature. Future work shall be extended by applying the symmetry features as constraints for the effective search of CH artifacts in digital repositories.