What is Scale-Invariant Feature Transform (SIFT)?

SIFT (Scale-Invariant Feature Transform) is a robust computer vision algorithm developed by David Lowe, widely used for feature detection, object recognition, and image matching due to its ability to handle scaling, rotation, and minor variations in illumination or viewpoint. The algorithm's primary goal is to identify distinctive and invariant keypoints in images, generating descriptors that facilitate keypoint matching across different images. SIFT achieves scale invariance by detecting the same features irrespective of the image's scale and maintains rotation invariance by ensuring keypoints remain consistent even when the image is rotated. The process involves several steps including scale-space extrema detection, where keypoints are identified across varying scales of the image; keypoint localization, which refines keypoint locations; orientation assignment, which provides each keypoint with a direction; and keypoint descriptor creation, which captures local image information in a compact form. OpenCV supports SIFT implementation, offering functions for keypoint detection and descriptor computation, which are crucial for various applications such as object detection, image stitching, and motion tracking.