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Combining SIFT and Individual Entropy Correlation Coefficient for Image Registration

  • Gan Liu
  • Shengyong Chen
  • Xiaolong Zhou
  • Xiaoyan Wang
  • Qiu Guan
  • Hui Yu
Part of the Communications in Computer and Information Science book series (CCIS, volume 484)

Abstract

Image registration is an important topic in many fields including industrial image analysis systems, medical and remote sensing. To improve the registration accuracy, an image registration method that combines scale invariant feature transform and individual entropy correlation coefficient (SIFT-IECC) is proposed in this paper. First, scale invariant feature transform algorithm is applied to extract feature points to construct a transformation model. Then, a rough registration image is obtained according to the transformation model. The individual entropy correlation coefficient is used as the similarity measure to refine the rough registration image. Finally, the experimental results show the superior performance of the proposed SIFT-IECC registration method by comparing with the state-of-the-art methods.

Keywords

Image registration Scale invariant feature transform Individual entropy correlation coefficient 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Gan Liu
    • 1
  • Shengyong Chen
    • 1
  • Xiaolong Zhou
    • 1
  • Xiaoyan Wang
    • 1
  • Qiu Guan
    • 1
  • Hui Yu
    • 2
  1. 1.College of Computer Science and TechnologyZhejiang University of TechnologyHangzhouChina
  2. 2.School of ComputingUniversity of PortsmouthPortsmouthEngland

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