SIFT and SURF

Scale-Invariant Feature Transform (or SIFT) is one of the most popular algorithms in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999, and it is now a patent of the University of British Columbia.

The SIFT approach, for image feature generation, takes an image and transforms it into a "large collection of local feature vectors". Each of these feature vectors is invariant to any scaling, rotation or translation of the image. This approach shares many features with neuron responses in primate vision. To aid the extraction of these features the SIFT algorithm applies a 4 stage filtering approach: (1) Scale-Space Extrema Detection (2) Keypoint Localistaion (3) Orientation Assignment (4) Keypoint Descriptor.

Speeded Up Robust Features (SURF) is said to have similar performance to SIFT, while at the same time being faster.