Feature matching is a process used in computer vision and image processing to identify and compare specific characteristics or features in images. This technique helps in recognizing objects, tracking movements, and stitching images together. By detecting key points, such as edges or corners, algorithms can create a unique representation of an object, allowing for effective comparison between different images. In feature matching, algorithms like SIFT (Scale-Invariant Feature Transform) or ORB (Oriented FAST and Rotated BRIEF) are commonly employed. These algorithms extract features from images and match them based on similarity, enabling applications in areas like facial recognition, augmented reality, and robot navigation.