  Fast and robust image matching is a very important task with various
applications in computer vision and robotics. In this paper, we compare the
performance of three different image matching techniques, i.e., SIFT, SURF, and
ORB, against different kinds of transformations and deformations such as
scaling, rotation, noise, fish eye distortion, and shearing. For this purpose,
we manually apply different types of transformations on original images and
compute the matching evaluation parameters such as the number of key points in
images, the matching rate, and the execution time required for each algorithm
and we will show that which algorithm is the best more robust against each kind
of distortion. Index Terms-Image matching, scale invariant feature transform
(SIFT), speed up robust feature (SURF), robust independent elementary features
(BRIEF), oriented FAST, rotated BRIEF (ORB).
