Iterative closest SIFT formulation for robust feature matching

  • Authors:
  • Rafael Lemuz-López;Miguel Arias-Estrada

  • Affiliations:
  • Ciencias Computacionales, Instituto Nacional de Astrofísica Óptica y Electrónica, Puebla, Pue. C.P, México;Ciencias Computacionales, Instituto Nacional de Astrofísica Óptica y Electrónica, Puebla, Pue. C.P, México

  • Venue:
  • ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

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.