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. def apply(referenceMesh: TriangleMesh, gp: LowRankGaussianProcess[_3D, _3D]): StatisticalMeshModel

   

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

5. final def asInstanceOf[T0]: T0

   

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6. def augmentModel(model: StatisticalMeshModel, biasModel: GaussianProcess[_3D, _3D], numBasisFunctions: Int): StatisticalMeshModel

   

   Adds a bias model to the given statistical shape model

7. def clone(): AnyRef

   

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8. def createUsingPCA(dc: DataCollection): 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

9. def createUsingPCA(referenceMesh: TriangleMesh, fields: Seq[VectorField[_3D, _3D]]): StatisticalMeshModel

   

   Creates a new DiscreteLowRankGaussianProcess, where the mean and covariance matrix are estimated from the given transformations.

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

    

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11. def equals(arg0: Any): Boolean

    

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12. def finalize(): Unit

    

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

    

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14. def hashCode(): Int

    

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15. final def isInstanceOf[T0]: Boolean

    

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

    

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17. final def notify(): Unit

    

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18. final def notifyAll(): Unit

    

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

    

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20. def toString(): String

    

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21. final def wait(): Unit

    

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

    

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

    

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