What is Feature Matching?

Feature matching in computer vision is a technique for identifying corresponding keypoints between two images of the same scene or object, crucial for tasks like object recognition, image alignment, and motion tracking. Keypoints, which are distinctive features such as corners, edges, or blobs, are detected and matched across images, regardless of transformations like scaling or rotation. The process involves several steps: detecting keypoints using algorithms like SIFT, FAST, or ORB, computing descriptors that represent the local appearance around each keypoint, and matching these descriptors using methods like Brute-Force, K-Nearest Neighbors (KNN), or FLANN. To improve accuracy, filtering techniques such as Lowe’s Ratio Test are applied to eliminate incorrect matches. Feature matching has diverse applications in computer vision, including image stitching and 3D reconstruction.