Scale Invariant Feature Transform (SIFT): Performance and Application

In 2004, David G. Lowe published his paper “Distinctive Image Features from ScaleInvariant Keypoints” (Lowe, 2004, [2]), outlining a method he developed for finding distinctive, scale and rotation invariant features in images that can be used to perform matching between different views of an object or scene. His method, Scale-Invariant Feature Transform (SIFT) combines scale-space theory and feature detection, geared toward a broad variety of applications in the field of computer vision, such as object recognition and stereo correspondence. As a part of the course Advanced Image Analysis at the Technical University of Denmark (DTU), we conducted a mini-project where we 1) studied Lowe’s work, 2) tested SIFT, 3) implemented a portion of SIFT ourselves, and 4) applied SIFT (combined with RANSAC algorithm) to automatic image stitching and automatic calculation of the fundamental matrix.