Speech and Vision Lab

This paper addresses the problem of detecting axes of bilateral symmetry in images. In order to achieve robustness to variation in illumination, only edge-gradient information is used. To overcome the problem of edge breaks, a potential field is developed from the edge map which spreads the information in the image plane. Pairs of points in the image plane are made to vote for their axes of symmetry with some confidence values. To make the method robust to overlapping objects, only local features in the form of Taylor coefficients are used for quantifying symmetry. We define an axis of symmetry histogram, which is used to accumulate the weighted votes for all possible axes of symmetry. To reduce the computational complexity of voting, a hashing scheme is proposed, wherein pairs of points, whose potential fields are too asymmetric, are pruned by not being counted for the vote. Experimental results indicate that the proposed method is fairly robust to edge breaks and is able to detect symmetries even when only 0.05% of the possible pairs are used for voting.