Detecting Reflectional Symmetry of Binary Shapes Based on Generalized R-Transform

• Published in: 2022 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) pp. 1 - 6
• Main Authors: Nguyen, Thanh Tuan, Nguyen, Thanh Phuong, Tran, Thanh-Hai
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2022
• Subjects: Analytical models; binary shape; Biological system modeling; Detectors; Feature extraction; Image recognition; R-signature; Radon transform; reflectional symmetry detection; Shape; Transforms
• ISSN: 2770-6850
• DOI: 10.1109/MAPR56351.2022.9924894

Analyzing reflectionally symmetric features inside an image is one of the important processes for recognizing the peculiar appearance of natural and man-made objects, biological patterns, etc. In this work, we will point out an efficient detector of reflectionally symmetric shapes by addressing a class of projection-based signatures that are structured by a generalized \mathcal{R}_{fm}-transform model. To this end, we will firstly prove the \mathcal{R}_{fm^{-}}transform in accordance with reflectional symmetry detection. Then different corresponding \mathcal{R}_{fm}-signatures of binary shapes are evaluated in order to determine which the corresponding exponentiation of the \mathcal{R}_{fm}-transform is the best for the detection. Experimental results of detecting on single/compound contour-based shapes have validated that the exponentiation of 10 is the most discriminatory, with over 2.7% better performance on the multiple-axis shapes in comparison with the conventional one. Additionally, the proposed detector also outperforms most of other existing methods. This finding should be recommended for applications in practice.