Image registration method using Harris Corner and modified Hausdorff distance with near set

Image registration is extensively used in many application domains such as medical, remote sensing, computer vision etc. The basic purpose of image registration is to obtain finest geometrical and radio-metrically aligned image from temporal or multi-modal image sensors. In this study, a novel salient feature-based image registration scheme has been designed and implemented by establishing a set of rotation, scale invariant features and corresponding them by a confirmation buildup method using Harris Corner Detection technique. It is an invariant feature vector model containing control points used for affine transformation. A bi-feature vector mapping method has been developed to choose the effective control points. Once feature selection and correspondence is been established, the transformation constraints are approximated using Near Set and modified Hausdorff distance. The proposed algorithm is evaluated under affine transform (translation, rotation, scale) and corresponding image intensity variation. Experimental results demonstrate the superiority of our proposed registration algorithm compared to the existing state-of-art research works in terms of accuracy and robustness.