c

scalismo.registration

MeanHuberLossMetric

case class MeanHuberLossMetric[D, A](fixedImage: Field[D, A], movingImage: DifferentiableField[D, A], transformationSpace: TransformationSpace[D], sampler: Sampler[D], delta: Double = 1.345)(implicit evidence$1: NDSpace[D], evidence$2: Scalar[A]) extends MeanPointwiseLossMetric[D, A] with Product with Serializable

Image to image metric which applies the Huber Loss function to the pointwise pixel difference. The parameter delta defines the threshold. The Huber loss increases quadratically for values below this threshold and linearly for values above this threshold.

See also

SumOfPointwiseLossMetric.

Linear Supertypes
Serializable, Product, Equals, MeanPointwiseLossMetric[D, A], ImageMetric[D, A], RegistrationMetric[D], AnyRef, Any
Ordering
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  2. By Inheritance
Inherited
  1. MeanHuberLossMetric
  2. Serializable
  3. Product
  4. Equals
  5. MeanPointwiseLossMetric
  6. ImageMetric
  7. RegistrationMetric
  8. AnyRef
  9. Any
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new MeanHuberLossMetric(fixedImage: Field[D, A], movingImage: DifferentiableField[D, A], transformationSpace: TransformationSpace[D], sampler: Sampler[D], delta: Double = 1.345)(implicit arg0: NDSpace[D], arg1: Scalar[A])

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  6. val delta: Double
  7. def derivative(parameters: DenseVector[Double]): DenseVector[Double]

    Computes the derivative of the metric at the point given by the parameters.

    Computes the derivative of the metric at the point given by the parameters.

    Definition Classes
    MeanPointwiseLossMetric → RegistrationMetric
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  10. val fixedImage: Field[D, A]
    Definition Classes
    MeanHuberLossMetric → ImageMetric
  11. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  12. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  13. def lossFunction(v: A): Double
    Attributes
    protected
    Definition Classes
    MeanHuberLossMetric → MeanPointwiseLossMetric
  14. def lossFunctionDerivative(v: A): Double
    Attributes
    protected
    Definition Classes
    MeanHuberLossMetric → MeanPointwiseLossMetric
  15. val movingImage: DifferentiableField[D, A]
    Definition Classes
    MeanHuberLossMetric → ImageMetric
  16. val ndSpace: NDSpace[D]
    Definition Classes
    MeanPointwiseLossMetric → RegistrationMetric
  17. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  18. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  19. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  20. def productElementNames: Iterator[String]
    Definition Classes
    Product
  21. val sampler: Sampler[D]
  22. val scalar: Scalar[A]
    Definition Classes
    MeanHuberLossMetric → ImageMetric
  23. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  24. val transformationSpace: TransformationSpace[D]
    Definition Classes
    MeanHuberLossMetric → RegistrationMetric
  25. def value(parameters: DenseVector[Double]): Double

    Computes the metric value for the given parameter vector

    Computes the metric value for the given parameter vector

    Definition Classes
    MeanPointwiseLossMetric → RegistrationMetric
  26. def valueAndDerivative(parameters: DenseVector[Double]): ValueAndDerivative

    Computes value and derivative in one go.

    Computes value and derivative in one go. It should be the same as calling value and takeDerivative separately, but allows for more efficient implementations.

    Definition Classes
    MeanPointwiseLossMetric → RegistrationMetric
  27. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  28. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  29. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from MeanPointwiseLossMetric[D, A]

Inherited from ImageMetric[D, A]

Inherited from RegistrationMetric[D]

Inherited from AnyRef

Inherited from Any

Ungrouped