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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 clone(): AnyRef

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6.  final def eq(arg0: AnyRef): Boolean

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7.  val featureExtractor: FeatureExtractor
8.  def finalize(): Unit

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9.  def fit(targetImage: DiscreteImage[_3D, Float], searchPointSampler: SearchPointSampler, iterations: Int, config: FittingConfiguration = FittingConfiguration.Default, startingTransformations: ModelTransformations = noTransformations): Try[FittingResult]

   Perform an ASM fitting for the given target image.

   Perform an ASM fitting for the given target image. This is logically equivalent to calling fitIterator(...).last

   targetImage

   target image to fit to.

   searchPointSampler

   sampler that defines the strategy where profiles are to be sampled.

   iterations

   maximum number of iterations for the fitting.

   config

   fitting configuration (thresholds). If omitted, uses FittingConfiguration.Default

   startingTransformations

   initial transformations to apply to the statistical model. If omitted, no transformations are applied (i.e. the fitting starts from the mean shape, with no rigid transformation)

   returns

   fitting result after the given number of iterations

10.  def fitIterator(targetImage: DiscreteImage[_3D, Float], searchPointSampler: SearchPointSampler, iterations: Int, config: FittingConfiguration = FittingConfiguration.Default, initialTransform: ModelTransformations = noTransformations): Iterator[Try[FittingResult]]

    Perform iterative ASM fitting for the given target image.

    Perform iterative ASM fitting for the given target image. This is essentially the same as the fit method, except that it returns the full iterator, so every step can be examined.

    See also

    fit() for a description of the parameters.

11.  def fitIteratorPreprocessed(image: PreprocessedImage, searchPointSampler: SearchPointSampler, iterations: Int, config: FittingConfiguration = FittingConfiguration.Default, initialTransform: ModelTransformations = noTransformations): Iterator[Try[FittingResult]]

    Perform iterative ASM fitting for the given preprocessed image.

    Perform iterative ASM fitting for the given preprocessed image. This is essentially the same as the fitIterator method, except that it uses the already preprocessed image.

    See also

    fit() for a description of the parameters.

12.  final def getClass(): Class[_ <: AnyRef]

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    @native()

13.  final def isInstanceOf[T0]: Boolean

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14.  def mean(): ASMSample

    Returns the mean mesh of the shape model, along with the mean feature profiles at the profile points

15.  final def ne(arg0: AnyRef): Boolean

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16.  final def notify(): Unit

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17.  final def notifyAll(): Unit

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18.  val preprocessor: ImagePreprocessor
19.  def productElementNames: Iterator[String]

    Definition Classes

    Product

20.  val profiles: Profiles
21.  def sample()(implicit rand: Random): ASMSample

    Returns a random sample mesh from the shape model, along with randomly sampled feature profiles at the profile points

22.  def sampleFeaturesOnly()(implicit rand: Random): ASMSample

    Utility function that allows to randomly sample different feature profiles, while keeping the profile points Meant to allow to easily inspect/debug the feature distribution

23.  val statisticalModel: StatisticalMeshModel
24.  final def synchronized[T0](arg0: => T0): T0

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25.  def transform(rigidTransformation: RigidTransformation[_3D]): ActiveShapeModel

    Returns an Active Shape Model where both the statistical shape Model and the profile points distributions are correctly transformed according to the provided rigid transformation

26.  final def wait(): Unit

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

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    @throws(classOf[java.lang.InterruptedException])

28.  final def wait(arg0: Long): Unit

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    @throws(classOf[java.lang.InterruptedException]) @native()