Moment-Based Dense Correspondence Matching Robust to Image Variation

SIFT flow adopts SIFT descriptor to find correspondence between two images. However, SIFT flow is not robust to scale and rotation for dense corresponding matching. In this paper, we propose moment-based dense correspondence matching which is robust to image variation. First, we apply Zernike moments to SIFT descriptor, i.e. Moments of Gradients (MoG). Then, we combine SIFT flow with MoG for dense correspondence matching. Experimental results show that the proposed method achieves better performance in correspondence matching than SIFT flow.