Abstract
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 \(L_2\)-minimizing estimate (\(L_2E\)), 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.
Keywords
- Gaussian Mixture Model
- Transformation Model
- Tikhonov Regularization
- Iterative Close Point
- Reproduce Kernel Hilbert Space
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgement
This work was supported by National Natural Science Foundation of China (NSFC, No. 61103070).
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Wang, G., Wang, Z., Zhao, W., Zhou, Q. (2015). Robust Point Matching Using Mixture of Asymmetric Gaussians for Nonrigid Transformation. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_28
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DOI: https://doi.org/10.1007/978-3-319-16817-3_28
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