Image Stitching Implementation Using SIFT Algorithm

This text describes how to utilize MATLAB's SIFT (Scale-Invariant Feature Transform) tool for image stitching, supporting both horizontal (left-right) and vertical (top-bottom) alignment configurations. Image stitching represents a computational technique that merges multiple images into a larger composite image, commonly applied in panorama creation, advertising posters, and large-scale visual presentations. The SIFT algorithm operates by detecting distinctive keypoints and generating robust descriptors within images, enabling precise feature matching and alignment during the stitching process. Implementation typically involves key stages: keypoint detection using difference-of-Gaussian (DoG) pyramids, orientation assignment, descriptor generation (128-dimensional vectors), and feature matching with nearest-neighbor distance ratio tests. This technology finds extensive applications in computer vision and image processing domains, particularly in image fusion tasks where seamless blending is achieved through techniques like weighted averaging at overlap regions, and image registration where geometric transformations (affine/projective) are calculated using RANSAC-based homography estimation. The MATLAB implementation commonly employs functions like `detectSIFTFeatures()` for feature extraction and `matchFeatures()` with SSD or ratio test metrics for robust correspondence establishment.