Image Stitching Using Harris Corner Features

In this application, we employ Harris corner features to accomplish image stitching. The process begins with detecting distinctive corner points using the Harris corner detection algorithm, which calculates the corner response function based on image gradient covariance matrices. Key feature points are selected by applying non-maximum suppression and thresholding to identify prominent corners that serve as reliable anchor points for alignment. These identified feature points then undergo descriptor extraction and matching using techniques like SSD (Sum of Squared Differences) or NCC (Normalized Cross Correlation) to establish correspondences between overlapping image regions. The matching pairs facilitate the computation of a homography matrix through RANSAC (Random Sample Consensus) to estimate the optimal transformation parameters. The actual image stitching is performed by warping the source images using the calculated homography transformation and blending them together with techniques such as linear blending or multi-band blending to minimize seam artifacts. By leveraging Harris corner features, we achieve precise image alignment and seamless composition, resulting in a complete and accurate stitched output that maintains geometric consistency across the combined image.