ISVC 2006: Advances in Visual Computing pp 502-513 | Cite as
Iterative Closest SIFT Formulation for Robust Feature Matching
Conference paper
Abstract
This paper presents a new feture 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.
Keywords
Image Pair Scale Invariant Feature Transform Iterative Close Point Registration Error Iterative Close Point
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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