MATLAB Implementation of SIFT-Based Image Registration

The Scale-Invariant Feature Transform (SIFT) algorithm for image registration is critically important in computer vision applications. As one of the fundamental techniques in image processing and computer vision, SIFT operates by detecting distinctive keypoints in images and computing robust descriptors for each keypoint to enable accurate image alignment. From an implementation perspective, the algorithm typically involves four main stages: scale-space extrema detection using Difference-of-Gaussian (DoG) pyramids, keypoint localization with sub-pixel accuracy, orientation assignment based on local image gradients, and 128-dimensional descriptor generation. In MATLAB implementations, key functions like detectSURFFeatures or custom SIFT implementations would handle feature extraction, while matchFeatures would perform descriptor matching using Euclidean distance metrics. This algorithm has been extensively researched and applied across various domains, proving to be a highly effective and reliable method for image registration tasks. Learning SIFT algorithm implementation is essential for researchers and developers working in image processing and computer vision fields, particularly for understanding feature-based matching techniques that form the foundation for more advanced computer vision systems.