Title | Detecting Reflectional Symmetry of Binary Shapes Based on Generalized R-Transform |
Publication Type | Conference Paper |
Year of Publication | 2022 |
Authors | Nguyen, Thanh Tuan, Nguyen, Thanh Phuong, Tran, Thanh-Hai |
Conference Name | 2022 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) |
Keywords | Analytical models, binary shape, Biological system modeling, Detectors, exponentiation, feature extraction, human factors, image recognition, pubcrawl, R-signature, Radon transform, reflectional symmetry detection, resilience, Resiliency, Scalability, Shape, Transforms |
Abstract | 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 \textbackslashmathcalR\_fm-transform model. To this end, we will firstly prove the \textbackslashmathcalR\_fm^-transform in accordance with reflectional symmetry detection. Then different corresponding \textbackslashmathcalR\_fm-signatures of binary shapes are evaluated in order to determine which the corresponding exponentiation of the \textbackslashmathcalR\_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. |
DOI | 10.1109/MAPR56351.2022.9924894 |
Citation Key | nguyen_detecting_2022 |