Robust Frame-to-Frame Hybrid Matching

In this paper, we propose a hybrid approach for addressing feature-based matching problem. We aim to obtain robust and accurate correspondence between features from image frames under unknown and unstructured environments. The approach incorporates image texture analysis, 2-D analytic signal theory and color modeling. It takes advantage of geometric invariant property in texture and monogenic signal information as well as photometric invariant property in HSV color information. The detected features are well localized with high accuracy and the selected matches are robust to changes in scale, blur, viewpoint, and illumination. Experiments conducted on a standard benchmark dataset demonstrate the effectiveness and reliability of our approach.