PointDistributionModel

scalismo.statisticalmodel.PointDistributionModel$
See thePointDistributionModel companion class

Attributes

Companion:
class
Graph
Supertypes
class Object
trait Matchable
class Any
Self type

Members list

Concise view

Value members

Concrete methods

def apply[D : NDSpace, PointRepr <: (DiscreteDomain)](reference: PointRepr[D], gp: LowRankGaussianProcess[D, EuclideanVector[D]])(implicit evidence$2: NDSpace[D], canWarp: DomainWarp[D, PointRepr], vectorizer: Vectorizer[EuclideanVector[D]]): PointDistributionModel[D, PointRepr]

creates a StatisticalMeshModel by discretizing the given Gaussian Process on the points of the reference mesh.

creates a StatisticalMeshModel by discretizing the given Gaussian Process on the points of the reference mesh.

Attributes

def augmentModel[D : NDSpace, DDomain <: (DiscreteDomain)](model: PointDistributionModel[D, DDomain], biasModel: LowRankGaussianProcess[D, EuclideanVector[D]])(implicit evidence$4: NDSpace[D], canWarp: DomainWarp[D, DDomain], vectorizer: Vectorizer[EuclideanVector[D]]): PointDistributionModel[D, DDomain]

Adds a bias model to the given statistical shape model

Adds a bias model to the given statistical shape model

Attributes

def createUsingPCA[D : NDSpace, DDomain <: (DiscreteDomain)](dc: DataCollection[D, DDomain, EuclideanVector[D]], stoppingCriterion: StoppingCriterion)(implicit evidence$5: NDSpace[D], canWarp: DomainWarp[D, DDomain], vectorizer: Vectorizer[EuclideanVector[D]]): PointDistributionModel[D, DDomain]

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

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.

Attributes

def createUsingPCA[D : NDSpace, DDomain <: (DiscreteDomain)](reference: DDomain[D], fields: Seq[Field[D, EuclideanVector[D]]], stoppingCriterion: StoppingCriterion)(implicit evidence$6: NDSpace[D], canWarp: DomainWarp[D, DDomain], vectorizer: Vectorizer[EuclideanVector[D]]): PointDistributionModel[D, DDomain]

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.

Attributes