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Iterative Closest SIFT Formulation for Robust Feature Matching

  • Rafael Lemuz-López
  • Miguel Arias-Estrada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4292)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rafael Lemuz-López
    • 1
  • Miguel Arias-Estrada
    • 1
  1. 1.Ciencias ComputacionalesInstituto Nacional de Astrofísica Óptica y ElectrónicaPueblaMéxico

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