Image Stitching using SIFT Feature Points

Image stitching is a technique that achieves seamless merging of multiple images by matching and aligning their feature points. During the image stitching process, the first critical step involves detecting distinctive feature points using algorithms like SIFT (Scale-Invariant Feature Transform), which identifies keypoints invariant to scale and rotation. The implementation typically uses OpenCV's cv2.SIFT_create() function to extract these feature points and their descriptors. Following feature detection, the matching phase employs algorithms such as FLANN (Fast Library for Approximate Nearest Neighbors) or brute-force matchers to find corresponding points between images. A key implementation detail involves using the cv2.BFMatcher() or cv2.FlannBasedMatcher() functions with distance metrics like Euclidean distance to establish reliable matches. After successful feature matching, the homography matrix is calculated using RANSAC (Random Sample Consensus) algorithm through cv2.findHomography() to estimate the geometric transformation between images. The final stitching operation utilizes perspective transformation via cv2.warpPerspective() to merge the images into a panoramic view. This technique enables the creation of larger composite images from multiple inputs and finds extensive applications in computer vision, photogrammetry, and image processing systems.