MATLAB Code Implementation of Image Matching Algorithms

Image matching algorithms refer to computational methods that identify and locate corresponding targets within reference images based on given input images to achieve matching objectives. As a crucial research direction in computer vision, these algorithms find applications across multiple domains including object detection, image recognition, and robotic navigation. Typical MATLAB implementations involve key stages such as feature extraction using detectors like SIFT or SURF, feature matching through similarity measures (e.g., Euclidean distance for feature descriptors), and spatial transformation estimation using RANSAC for outlier removal. Through continuous improvement and optimization of image matching algorithms - including enhancements in feature invariance, matching efficiency, and robustness to occlusion - matching accuracy and computational efficiency can be significantly improved, thereby enabling broader applications of image matching across various technical domains.