Image Registration Based on SIFT Features with MATLAB Implementation

This MATLAB source code implements image registration using Scale-Invariant Feature Transform (SIFT) methodology, enabling precise alignment operations between images. The algorithm incorporates multiple sophisticated stages including keypoint detection using Difference of Gaussians (DoG), feature descriptor extraction with orientation assignment, and feature matching using nearest-neighbor distance ratio. The implementation includes geometric transformation estimation through RANSAC-based homography computation to ensure robust matching. The code is designed for various image processing tasks such as image stitching, feature matching, and geometric correction. Due to SIFT's inherent advantages of rotation invariance and scale invariance, this algorithm delivers reliable performance across diverse imaging conditions and transformations. Key implementation aspects include: - Automated feature detection using vl_sift function or custom DoG implementation - Feature matching with Lowe's ratio test for outlier rejection - Homography matrix estimation for spatial transformation - Warping and blending functions for seamless image composition The code structure follows modular design principles, allowing easy customization of parameters like feature threshold, matching criteria, and transformation types. This implementation serves as a comprehensive solution for researchers and developers working on computer vision projects requiring robust image alignment capabilities.