Trait

scalismo.statisticalmodel.asm

FeatureExtractor

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trait FeatureExtractor extends (PreprocessedImage, Point[_3D], TriangleMesh, PointId) ⇒ Option[DenseVector[Float]] with HasIOMetadata

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HasIOMetadata, (PreprocessedImage, Point[_3D], TriangleMesh, PointId) ⇒ Option[DenseVector[Float]], AnyRef, Any
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  2. HasIOMetadata
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Abstract Value Members

  1. abstract def apply(image: PreprocessedImage, featurePoint: Point[_3D], mesh: TriangleMesh, profilePointId: PointId): Option[DenseVector[Float]]

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    Actually extracts features from an image.

    Actually extracts features from an image. The image and featurePoint arguments are arguably necessary for all feature extractor implementations. The additional two parameters (mesh and point id) are passed in because the feature extractor might need that information. For instance, the NormalDirectionFeatureExtractor needs that information to determine the "spatial orientation" of the line on which the image points to be examined have to lie.

    image

    the image to extract features in

    featurePoint

    the point in space to extract features at

    mesh

    a mesh instance in correspondence with the image

    profilePointId

    a point id on the mesh, corresponding to a profiled point id.

    Definition Classes
    FeatureExtractor → Function4
  2. abstract def featurePoints(mesh: TriangleMesh, profilePointId: PointId, featurePoint: Point[_3D]): Option[IndexedSeq[Point[_3D]]]

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    Return the points at which feature components are extracted for a given mesh and point.

    Return the points at which feature components are extracted for a given mesh and point. This is mainly intended for visualization, and only really makes sense if there exists a one-to-one correspondence between such points and the actual feature components returned by the apply().

    In other words: if a particular implementation uses a method for determining features where there is no correspondence between the feature components and particular points, the method should return None. Otherwise, it should return Some(IndexedSeq(...)), where the length of the sequence matches the length of the feature vector.

    For the difference between profilePointId and featurePoint, see the documentation of the apply method.

    mesh

    the mesh to determine the feature points for

    profilePointId

    the profile point id to determine the feature points for

    featurePoint

    the actual point in space where the features are to be extracted

    returns

    a sequence of points, or None if there is no sensible correspondence between feature and points, as outlined above.

  3. abstract def ioMetadata: IOMetadata

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    Definition Classes
    HasIOMetadata

Concrete 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 clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  6. def curried: (PreprocessedImage) ⇒ (Point[_3D]) ⇒ (TriangleMesh) ⇒ (PointId) ⇒ Option[DenseVector[Float]]

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    Definition Classes
    Function4
    Annotations
    @unspecialized()
  7. final def eq(arg0: AnyRef): Boolean

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )
  10. final def getClass(): Class[_]

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  11. def hashCode(): Int

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  12. final def isInstanceOf[T0]: Boolean

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

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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

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  17. def toString(): String

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    Function4 → AnyRef → Any
  18. def tupled: ((PreprocessedImage, Point[_3D], TriangleMesh, PointId)) ⇒ Option[DenseVector[Float]]

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    Function4
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    @unspecialized()
  19. final def wait(): Unit

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    @throws( ... )
  20. final def wait(arg0: Long, arg1: Int): Unit

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

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Inherited from HasIOMetadata

Inherited from (PreprocessedImage, Point[_3D], TriangleMesh, PointId) ⇒ Option[DenseVector[Float]]

Inherited from AnyRef

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