Image Matching by Affine Speed-Up Robust Features

Affine invariant image comparison is always consequential in computer vision. In this paper, affine-SURF (ASURF) is introduced. Through a series of affine transformations and feature extraction, the matching algorithm becomes more robust with the view and scale change. A kd-tree structure is build to store the feature sets and BBF search algorithm is used in feature matching, then duplicates are removed by the conditional of Euclidean distance ratio. Experiments show it has a good result, comparisons with SIFT and SURF is made to prove its performance.