Visible to the public Detecting Reflectional Symmetry of Binary Shapes Based on Generalized R-Transform

TitleDetecting Reflectional Symmetry of Binary Shapes Based on Generalized R-Transform
Publication TypeConference Paper
Year of Publication2022
AuthorsNguyen, Thanh Tuan, Nguyen, Thanh Phuong, Tran, Thanh-Hai
Conference Name2022 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)
KeywordsAnalytical 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
AbstractAnalyzing 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.
DOI10.1109/MAPR56351.2022.9924894
Citation Keynguyen_detecting_2022