SIFT Feature Extraction Source Code

This program extracts SIFT (Scale-Invariant Feature Transform) features from an input image through a multi-stage algorithm implementation. The implementation includes keypoint detection using Difference-of-Gaussian (DoG) pyramid construction, orientation assignment, and 128-dimensional descriptor generation. Detected feature points are annotated on the original image using marker visualization techniques, providing enhanced feature localization information. Additionally, the program implements image matching and registration functionality between multiple images. This is achieved through feature descriptor comparison using distance metrics (typically Euclidean distance) and ratio testing for robust matching. The registration process employs geometric transformation estimation (such as homography matrix calculation) to align images based on matched feature pairs. The code structure is organized modularly with clear separation between feature detection, description, matching, and visualization components. Key functions include SIFT detector initialization, descriptor computation, and matching algorithm implementation using k-nearest neighbor search. The implementation follows standard SIFT methodology while maintaining readability, making it particularly suitable for individuals interested in image processing and computer vision, especially those seeking to understand SIFT features and image matching techniques at an implementation level.