Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. For any object in an image, interesting points on the object can be extracted to provide a “feature description” of the object. The algorithm was published by David Lowe in 1999.This paper gives a review and describes how SIFT extract the image features that have many properties that make them suitable for matching differing images of an object or scene. The features are invariant to image scaling and rotation, and partially invariant to change in illumination and 3D camera view point . They are well localized in both the spatial and frequency domains, reducing the probability of disruption by occlusion, clutter, or noise. Large numbers of features can be extracted from typical images with efficient algorithms.
. Lowe, David G. (1999). “Object recognition from local scale-invariant features”. Proceedings of the International Conference on Computer Vision 2. pp. 1150–1157.
. “Method and apparatus for identifying scale invariant features in an image and use of same for locating an object in an image”, David Lowe’s patent for the SIFT algorithm, March 23, 2004.