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When the regularized kernel methods are utilized in the mismatch removal problem, the regularization coefficient and the choice of kernel function will seriously affect the performance of the methods. In this paper, we propose a method that combines an improved regularization and an adaptive Gaussian kernel function to interpolate the vector fields so as to overcome the issue. We formulated the problem as a modified maximum a posterior estimation of a Bayesian model. In this model, a two-order term of the regularization coefficient is introduced into the regularized risk function in order that the coefficient can be adaptively estimated in the expectation-maximization algorithm. In addition, an adaptive Gaussian kernel function also is imposed to construct the regularization, in which the width of the kernel function is adaptively determined by the diagonal length of the maximum enveloping rectangle of the sample set. Our experimental results verified that our method was robust to large outlier percentages and was slightly superior to some state-of-the-art methods in precision-recall tradeoff and efficiency. The evidence that the performance of our method was insensitive to the remaining inner parameters verified its good self-adaptability. Finally, airborne image pairs were used to demonstrate that our method can establish the feature correspondences even under a discontinuous vector field scene. In addition, we found that our method can obtain higher precision given a residual threshold for special applications such as robust epipolar geometry estimation in computer vision and photogrammetry.

Adaptive Image Mismatch Removal With Vector Field Interpolation Based on Improved Regularization and Gaussian Kernel Function