Phase singularities for image representation and matching

Phase features are widely used in image processing and representation due to their stability to deformation and noise. However, phase singularities,where the signals vanish, are generally regarded as harmful and unreliable facts. In this paper, on the contrary, we will show that phase singularities calculated by Laguerre-Gauss filter contain important information of input image and can provide a reliable representation for image matching. We show that the positions of phase singularities are invariant to translation and rotation. Usually, it is possible to recover the input image up to a constant scaling only from the positions of phase singularities. We study phase singularities in scale space, which allows us to determine the "intrinsic scales" of key phase singularities. We introduce three physical measures of the local structures of phase singularities and combine these measures with SIFT descriptor for image matching. We execute experiments on benchmark database to examine the proposed methods. The results indicate that the proposed method can achieve comparable performance with certain well-known methods.