In this paper, we present a novel robust method for point matching under noise, deformation, occlusion and outliers. We introduce a new probability model to represent point sets, namely asymmetric Gaussian (AG), which can capture spatially asymmetric distributions. Firstly, we use a mixture of AGs to represent the point set. Secondly, we use L2-minimizing estimate (L2E), a robust estimator to estimate densities between two point sets, to estimate the transformation function in reproducing kernel Hilbert space (RKHS) with regularization theory. Thirdly, we use low-rank kernel matrix approximation to reduce the computational complexity. Experimental results show that our method outperforms the comparative state-of-the-art methods on most scenarios, and it is quite robust to noise, deformation, occlusion and outliers.
CITATION STYLE
Wang, G., Wang, Z., Zhao, W., & Zhou, Q. (2015). Robust point matching using mixture of asymmetric gaussians for nonrigid transformation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9006, pp. 433–444). Springer Verlag. https://doi.org/10.1007/978-3-319-16817-3_28
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