MATLAB Implementation of SIFT-Based Image Stitching

The process of image stitching using the SIFT algorithm involves several key steps implemented through MATLAB functions. First, feature points are extracted from both input images using the SIFT detector, which identifies distinctive keypoints based on scale-space extrema and orientation assignment. The algorithm then computes feature descriptors for these keypoints, typically represented as 128-dimensional vectors capturing local gradient information. Next, feature matching is performed using techniques like k-nearest neighbors or brute-force matchers to find corresponding points between images. The RANSAC (Random Sample Consensus) algorithm is commonly applied to estimate the homography transformation matrix while filtering out outlier matches. This matrix defines the projective transformation that aligns the second image with the first. Finally, the second image undergoes projective transformation using the calculated homography matrix. The transformed image is then blended with the first image using techniques like linear blending or multi-band blending to create a seamless panorama. The implementation typically involves MATLAB's Computer Vision Toolbox functions for feature detection, matching, and geometric transformation operations.