Image Stitching with Combined Moment Invariants and Sift Features

Image stitching is used to combine multiple photographic images from camera network with overlapping field of view to produce panoramic view. With image stitching, the view is enlarged and the amount of information increases with the no. of images that are stitched. In the existing methods, the whole images from the adjacent views are considered thus leads to increase in both time and computational complexity. In this paper, an approach for image stitching using invariant moments combined with SIFT features is presented to reduce the time and computational complexity. It is observed that only a small portion of the adjacent view images are overlapped. Hence, the proposed method aims in detecting overlapping portion for extracting matching points. The overlapping regions are determined using gradient based dominant edge extraction and invariant moments. In the deduced region, the SIFT (Shift Invariant Feature Transform) features are extracted to determine the matching features. The registration is carried on with RANSAC (Random Sample Consensus) algorithm and final output mosaic is obtained by warping the images. The proposed approach results in reduced time and computational when compared to existing methods.