A Featured Area-Based Image Registration

Abstract—Image registration is necessary when images from multiple viewpoints need be brought into common coordinate system. Image registration techniques can be classified as area-based methods and feature-based methods. In area-based methods, no features are selected and regularly tessellated areas are usually used for matching. In feature-based methods, features such as regions, lines, and prominent points are detected and used for matching. When image contains rich features, feature-based methods are preferred and when it does not, area-based methods are usually adopted. There are occasions where richness of features varies locally in the image. In this case, either area-based methods or feature-based methods alone may not generate successful results. In this paper, we propose a mixture of two methods termed as featured area-based method. In the proposed, we first tessellate the image into equal-sized areas, estimate richness of features of each area utilizing the edge direction histogram, choose only those areas with a certain level of richness, and use them for matching. We compared the proposed with well-known conventional methods such as Kanade-Lucas-Tomasi(KLT) method, speeded up robust features (SURF), and scale-invariant feature transform (SIFT), and showed that the proposed performs better than others.