object StatisticalMeshModel extends Serializable
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- def apply(referenceMesh: TriangleMesh[_3D], gp: LowRankGaussianProcess[_3D, EuclideanVector[_3D]]): StatisticalMeshModel
creates a StatisticalMeshModel by discretizing the given Gaussian Process on the points of the reference mesh.
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- def augmentModel(model: StatisticalMeshModel, biasModel: LowRankGaussianProcess[_3D, EuclideanVector[_3D]]): StatisticalMeshModel
Adds a bias model to the given statistical shape model
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- def createUsingPCA(referenceMesh: TriangleMesh[_3D], fields: Seq[Field[_3D, EuclideanVector[_3D]]], stoppingCriterion: StoppingCriterion): StatisticalMeshModel
Creates a new Statistical mesh model, with its mean and covariance matrix estimated from the given fields.
Creates a new Statistical mesh model, with its mean and covariance matrix estimated from the given fields.
Per default, the resulting mesh model will have rank (i.e. number of principal components) corresponding to the number of linearly independent fields. By providing an explicit stopping criterion, one can, however, compute only the leading principal components. See PivotedCholesky.StoppingCriterion for more details.
- def createUsingPCA(dc: TriangleMeshDataCollection[_3D], stoppingCriterion: StoppingCriterion = PivotedCholesky.RelativeTolerance(0)): Try[StatisticalMeshModel]
Returns a PCA model with given reference mesh and a set of items in correspondence.
Returns a PCA model with given reference mesh and a set of items in correspondence. All points of the reference mesh are considered for computing the PCA
Per default, the resulting mesh model will have rank (i.e. number of principal components) corresponding to the number of linearly independent fields. By providing an explicit stopping criterion, one can, however, compute only the leading principal components. See PivotedCholesky.StoppingCriterion for more details.
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