Image Matching Using SIFT Algorithm with Implementation Enhancements

This paper introduces an image matching methodology utilizing the SIFT (Scale-Invariant Feature Transform) algorithm. The implementation begins with detailed explanations of SIFT's core principles and workflow, including keypoint detection through difference-of-Gaussian pyramids, orientation assignment, and 128-dimensional descriptor generation. The matching process employs nearest neighbor search with threshold-based Lowe's ratio test for initial feature correspondence. To enhance matching accuracy, we integrate RANSAC (Random Sample Consensus) algorithm which iteratively estimates transformation models using minimal sample sets while rejecting outliers through consensus validation. The implementation typically involves: 1) Randomly selecting minimal point pairs (e.g., 4 matches for homography estimation), 2) Computing transformation matrices using OpenCV's findHomography function, 3) Calculating inliers based on reprojection error thresholds, and 4) Refining the model with maximum inlier consensus over multiple iterations. Subsequently, we demonstrate practical image stitching applications where matched features are used to compute projective transformations (homographies) between image pairs. The stitching pipeline involves: warping images onto a common canvas using cv2.warpPerspective, multi-band blending for seamless transitions, and result validation through visual inspection. Experimental results show how multiple images can be combined to create panoramic representations with preserved details and extended field of view. Overall, this work presents a comprehensive technical framework for SIFT-based image matching and stitching, highlighting its robustness through RANSAC integration and demonstrating practical implementation approaches for computer vision applications.