Image Stitching Implementation using MATLAB

This document presents a MATLAB program designed for image stitching through feature extraction, matching, and fusion. The implementation follows a systematic pipeline: first, the Scale-Invariant Feature Transform (SIFT) algorithm detects distinctive keypoints and computes their descriptors, providing rotation and scale invariance. The code then performs initial feature matching using nearest-neighbor search, typically employing Euclidean distance metrics to find correspondences between images. To enhance matching accuracy, the program incorporates Random Sample Consensus (RANSAC) for robust outlier rejection. This iterative algorithm estimates geometric transformations (such as homography matrices) while identifying and eliminating erroneous matches, ensuring alignment precision. The MATLAB implementation likely uses vision.GeometricTransformEstimator or similar functions for this optimization step. Finally, the validated correspondences guide the image fusion process, where transformation parameters align the images geometrically. The blending stage employs techniques like alpha blending or multiband blending to minimize seams and exposure differences, creating a coherent composite image. This comprehensive approach provides an efficient and accurate solution for image processing applications, suitable for panoramic photography, medical imaging, and computer vision tasks.