Scale-invariant feature transform
Scale-Invariant Feature Transform (or SIFT) is a computer vision algorithm for extracting distinctive features from images, to be used in algorithms for tasks like localization, stereo vision, and object recognition.
The algorithm was devised by David Lowe, who has a patent on it.
SIFT is a fundamental part of the Visual Pattern Recognition (ViPR) and visual Simultaneous Localization and Mapping (vSLAM) algorithms developed by Evolution Robotics.
The feature representations found by SIFT are thought to be analogous to those of neurons in inferior temporal cortex, a region used for object recognition in primate vision.