title: A mixture model for robust point matching under multi-layer motion.
abstract: This paper proposes an efficient mixture model for establishing robust point correspondences between two sets of points under multi-layer motion. Our algorithm starts by creating a set of putative correspondences which can contain a number of false correspondences, or outliers, in addition to the true correspondences (inliers). Next we solve for correspondence by interpolating a set of spatial transformations on the putative correspondence set based on a mixture model, which involves estimating a consensus of inlier points whose matching follows a non-parametric geometrical constraint. We formulate this as a maximum a posteriori (MAP) estimation of a Bayesian model with hidden/latent variables indicating whether matches in the putative set are outliers or inliers. We impose non-parametric geometrical constraints on the correspondence, as a prior distribution, in a reproducing kernel Hilbert space (RKHS). MAP estimation is performed by the EM algorithm which by also estimating the variance of the prior model (initialized to a large value) is able to obtain good estimates very quickly (e.g., avoiding many of the local minima inherent in this formulation). We further provide a fast implementation based on sparse approximation which can achieve a significant speed-up without much performance degradation. We illustrate the proposed method on 2D and 3D real images for sparse feature correspondence, as well as a public available dataset for shape matching. The quantitative results demonstrate that our method is robust to non-rigid deformation and multi-layer/large discontinuous motion.
authors.affiliation: National Key Laboratory of Science & Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China.
authors.forename: Jiayi
authors.initials: J
authors.lastname: Ma
authors.affiliation: Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Chen
authors.affiliation: National Key Laboratory of Science & Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China.
authors.forename: Delie
authors.initials: D
authors.lastname: Ming
authors.affiliation: National Key Laboratory of Science & Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China.
authors.forename: Jinwen
authors.initials: J
authors.lastname: Tian
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008962:Models, Theoretical; D009038:Motion; D015203:Reproducibility of Results