Lowe's Scale-Invariant Feature Transform (SIFT)

Lowe's Scale-Invariant Feature Transform (SIFT) is a computer vision technique that identifies and describes unique, stable key points in images, such as corners or edges. It captures features that remain consistent even when images are scaled, rotated, or taken from different angles. By doing so, SIFT enables reliable matching and recognition of objects across varied images. This makes it valuable for applications like image recognition, object tracking, and 3D modeling, providing robustness against changes in size or viewpoint.