MATLAB Code Implementation for Image Matching

The image matching implementation is achieved through several key programs: 1. match.m: This testing program validates the accuracy of image matching by comparing feature points between two images. It typically includes functions for loading images, calling SIFT feature extraction, and evaluating matching results using distance metrics between descriptors. 2. sift.m: This core algorithm implements Scale-Invariant Feature Transform (SIFT), which detects keypoints and computes their descriptors. The implementation involves multiple stages: scale-space extrema detection, keypoint localization, orientation assignment, and generating 128-dimensional feature vectors that are invariant to scale and rotation. 3. appendimages.m: This utility function creates a new composite image containing both input images side by side, with connecting lines drawn between matched feature pairs. The function handles image concatenation, coordinate mapping, and line drawing operations to visualize matching results effectively. Through the integration of these key programs, we can achieve complete image matching functionality and obtain visual matching results. The code structure allows for modular testing and optimization of each component while maintaining overall system coherence. This implementation provides a detailed framework for understanding and executing image matching processes using feature-based methods.