The SIFT (Scale-Invariant Feature Transform) method extracts distinctive features from an image, making it robust to scale, rotation, and illumination changes. It begins by detecting keypoints using a difference-of-Gaussian (DoG) method to identify regions of interest across multiple scales.
Each keypoint’s neighborhood is then described by a feature vector. Orientation histograms are computed around the keypoints, capturing the gradient information and providing scale and rotation invariance.
These feature descriptors are used for tasks like image matching and object recognition. Despite being computationally intensive, SIFT remains popular due to its robustness and accuracy in diverse applications.