A Novel Kernel Correlation Model with the Correspondence Estimation

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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