SIFT-Based Image Feature Point Extraction and Image Stitching

This article presents a methodology for image feature point extraction and image stitching based on the Scale-Invariant Feature Transform (SIFT) algorithm. The SIFT algorithm effectively detects distinctive keypoints in images and matches them across different views for seamless stitching. The implementation includes ready-to-run code with sample images, enabling users to immediately test and understand the practical application. Key components of the code involve feature detection using Difference of Gaussians (DoG), keypoint localization with sub-pixel accuracy, orientation assignment for rotation invariance, and descriptor generation using gradient histograms. For image stitching, the implementation utilizes RANSAC (Random Sample Consensus) for robust homography estimation and multi-band blending for smooth transitions between stitched images. Through this approach, users can efficiently identify stable feature points across multiple images and combine them into a complete panoramic view. This technique has been widely adopted in computer vision and image processing applications, demonstrating robust performance across various real-world scenarios including panoramic photography, medical imaging, and surveillance systems.