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A Novel Kernel Correlation Model with the Correspondence Estimation

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Abstract

We present a novel multiple-linked iterative closest point method to estimate correspondences and the rigid/non-rigid transformations between point-sets or shapes. The estimation task is carried out by maximizing a symmetric similarity function, which is the product of the square roots of correspondences and a kernel correlation. The local mean square error analysis and robustness analysis are provided to show our method’s superior performance to the kernel correlation method.

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Authors and Affiliations

  1. Mathematics, National Taiwan University, Taipei, Taiwan

    Pengwen Chen

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  1. Pengwen Chen
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Correspondence to Pengwen Chen.

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Chen, P. A Novel Kernel Correlation Model with the Correspondence Estimation. J Math Imaging Vis 39, 100–120 (2011). https://doi.org/10.1007/s10851-010-0230-6

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