Dependent landmark drift: robust point set registration based on the Gaussian mixture model with a statistical shape model
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Point set registration is to find point-by-point correspondences between point sets, each of which characterizes the shape of an object. Due to the assumption of the local preservation of object geometry, prevalent algorithms in the area can often elegantly solve the problems without using geometric information specific to the objects. This means that registration performance can be further improved by using the prior knowledge of object geometry. In this paper, we propose a novel point set registration method using the Gaussian mixture model with prior shape information encoded as a statistical shape model. Our transformation model is defined as the combination of the rigid transformation, the motion coherence, and the statistical shape model. Therefore, the proposed method works effectively if the target point set includes outliers and missing regions, or if it is rotated. The computational cost can be reduced to linear and thereby the method is scalable to large point sets. The effectiveness of the method will be verified through comparisons with existing algorithms using datasets concerning human hands and faces.
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