StatisticalMeshModel

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

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5. def changeReference(t: (Point[_3D]) ⇒ Point[_3D]): StatisticalMeshModel

   

   Warps the reference mesh with the given transform.

   Warps the reference mesh with the given transform. The space spanned by the model is not affected.

6. def clone(): AnyRef

   

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   @throws( ... )

7. def coefficients(mesh: TriangleMesh): DenseVector[Float]

   
   See also

   DiscreteLowRankGaussianProcess.coefficients

8. def cov(ptId1: PointId, ptId2: PointId): SquareMatrix[_3D]

   

   The covariance between two points of the mesh with given point id.

   The covariance between two points of the mesh with given point id.

   See also

   DiscreteLowRankGaussianProcess.cov

9. final def eq(arg0: AnyRef): Boolean

   

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10. def finalize(): Unit

    

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11. final def getClass(): Class[_]

    

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12. val gp: DiscreteLowRankGaussianProcess[_3D, _3D]

    

13. def instance(c: DenseVector[Float]): TriangleMesh

    

    returns a shape that corresponds to a linear combination of the basis functions with the given coefficients c.

    returns a shape that corresponds to a linear combination of the basis functions with the given coefficients c.

    See also

    DiscreteLowRankGaussianProcess.instance

14. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

15. def marginal(ptIds: IndexedSeq[PointId]): StatisticalMeshModel

    

    Returns a marginal StatisticalMeshModel, modelling deformations only on the chosen points of the reference

    Returns a marginal StatisticalMeshModel, modelling deformations only on the chosen points of the reference

    This method proceeds by clipping the reference mesh to keep only the indicated point identifiers, and then marginalizing the GP over those points. Notice that when clipping, not all indicated point ids will be part of the clipped mesh, as some points may not belong to any cells anymore. Therefore 2 behaviours are supported by this method :

    1- in case some of the indicated pointIds remain after clipping and do form a mesh, a marginal model is returned only for those points 2- in case none of the indicated points remain (they are not meshed), a reference mesh with all indicated point Ids and no cells is constructed and a marginal over this new reference is returned

    See also

    DiscreteLowRankGaussianProcess.marginal

16. lazy val mean: TriangleMesh

    

    The mean shape

    The mean shape

    See also

    DiscreteLowRankGaussianProcess.mean

17. final def ne(arg0: AnyRef): Boolean

    

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18. final def notify(): Unit

    

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19. final def notifyAll(): Unit

    

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20. def pdf(instanceCoefficients: DenseVector[Float]): Double

    

    returns the probability density for an instance of the model

    returns the probability density for an instance of the model

    instanceCoefficients

    coefficients of the instance in the model. For shapes in correspondence, these can be obtained using the coefficients method

21. def posterior(trainingData: IndexedSeq[(PointId, Point[_3D], NDimensionalNormalDistribution[_3D])]): StatisticalMeshModel

    

    Similar to Point[_3D], Double)], but the training data is defined by specifying the target point instead of the displacement vector

22. def posterior(trainingData: IndexedSeq[(PointId, Point[_3D])], sigma2: Double): StatisticalMeshModel

    

    Similar to Point[_3D])], sigma2: Double), but the training data is defined by specifying the target point instead of the displacement vector

23. def project(mesh: TriangleMesh): TriangleMesh

    
    See also

    DiscreteLowRankGaussianProcess.project

24. val rank: Int

    
    See also

    scalismo.statisticalmodel.DiscreteLowRankGaussianProcess.rank

25. val referenceMesh: TriangleMesh

    

26. def sample: TriangleMesh

    

    draws a random shape.

    draws a random shape.

    See also

    DiscreteLowRankGaussianProcess.sample

27. final def synchronized[T0](arg0: ⇒ T0): T0

    

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28. def transform(rigidTransform: RigidTransformation[_3D]): StatisticalMeshModel

    

    transform the statistical mesh model using the given rigid transform.

    transform the statistical mesh model using the given rigid transform. The spanned shape space is not affected by this operations.

29. final def wait(): Unit

    

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30. final def wait(arg0: Long, arg1: Int): Unit

    

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31. final def wait(arg0: Long): Unit

    

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    @throws( ... )