This paper presents a new feature matching algorithm. The proposed algorithm integrates the Scale Invariant Feature Transform (SIFT) local descriptor in the Iterative Closest Point (ICP) scheme. The new algorithm addresses the problem of finding the appropriate match between repetitive patterns that appear in manmade scenes. The matching of two sets of points is computed integrating appearance and distance properties between putative match candidates. To demonstrate the performance of the new algorithm, the new approach is applied on real images. The results show that the proposed algorithm increases the number of correct feature correspondences and at the same time reduces significantly matching errors when compared to the original SIFT and ICP algorithms.
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