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We present a novel method for automatic shape model building from a collection of training shapes.
Furthermore, the regressed shape is always constrained to reside in the linear subspace constructed by all training shapes.
Their nonparametric approach is based on the fact that the regressed shape is a linear combination of all training shapes.
We observe smooth transitions between the interpolated shapes and training shapes, especially around the wheels of the targets.
Cremers et al. [18] implement a shape model using a kernel density estimator to produce a statistical shape model that can model fairly distinct training shapes.
The shape model is trained employing Principal Components Analysis (PCA) on the aligned training shapes to find the eigenshapes of maximum variance and the mean shape s0.
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