Abstract We propose a new point set registration method based on mixture framework and variational inference. A three-phase registration strategy (TRS) is proposed to automatically process point set registration problem in different cases. A Gaussian variational mixture model (GVMM) with isotropic and anisotropic components under the variational inference framework is designed to weaken the effect of outliers. The Dirichlet distribution is applied to govern the mixture proportion of Gaussian components and then distinguishes missing points. We test the performance of our method in contour registration, Graffiti images, retinal images, remote sensing images and 3D human motion, and compare with six state-of-the-art methods. Our method shows favorable performances in most scenarios.
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Point set registration with mixture framework and variational inference
Semantic Scholar · Computer Science · 2020
Abstract
Abstract We propose a new point set registration method based on mixture framework and variational inference. A three-phase registration strategy (TRS) is proposed to automatically process point set registration problem in different cases. A Gaussian variational mixture model (GVMM) with isotropic and anisotropic components under the variational inference framework is designed to weaken the effect of outliers. The Dirichlet distribution is applied to govern the mixture proportion of Gaussian components and then distinguishes missing points. We test the performance of our method in contour registration, Graffiti images, retinal images, remote sensing images and 3D human motion, and compare with six state-of-the-art methods. Our method shows favorable performances in most scenarios.