Biblio

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\_fmˆ-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.