  Image identification is one of the most challenging tasks in different areas
of computer vision. Scale-invariant feature transform is an algorithm to detect
and describe local features in images to further use them as an image matching
criteria. In this paper, the performance of the SIFT matching algorithm against
various image distortions such as rotation, scaling, fisheye and motion
distortion are evaluated and false and true positive rates for a large number
of image pairs are calculated and presented. We also evaluate the distribution
of the matched keypoint orientation difference for each image deformation.
