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Find Feature Shapes from Triangle Geometry

Group (Subgroup)

Statistics (Morphological)


This Filter calculates the second-order moments of each enclosed Feature in a Triangle Geometry. The second-order moments allow for the determination of the principal axis lengths, pricipal axis directions, aspect ratios and moment invariant Omega3s. The principal axis lengths are those of a "best-fit" ellipsoid. The algorithm for determining the moments and these values is as follows:

  1. For each Triangle on the bounding surface of a Feature, construct a tetrahedron whose fourth vertex is the centroid of the Feature, ensuring normals are consistent (this Filter uses the convetion where normals point inwards; note that the actual winding of the Triangle Geometry is not modified)
  2. Subdivide each constructed tetrahedron into 8 smaller tetrahedra
  3. For each subdivided tetrahedron, compute the distance from that tetrahedron's centroid to the centroid of the parent Feature
  4. For each subdivided tetrahedron, calculate Ixx, Iyy, Izz, Ixy, Ixz and Iyz using the x, y and z distances determined in step 1
  5. Use the relationship of principal moments to the principal axis lengths for an ellipsoid, which can be found in [4], to determine the Axis Lengths
  6. Calculate the Aspect Ratios from the Axis Lengths found in step 5.
  7. Determine the Euler angles required to represent the principal axis directions in the sample reference frame and store them as the Feature's Axis Euler Angles.
  8. Calculate the moment invariant Omega3 as definied in [2] and is discussed further in [1] and [3]

Note: Due to the method used to subdivide the tetrahedra, some sharp corners of shapes may not be properly represented, resulting in inaccurate Omega3 values. This problem is especially apparent for perfect rectangular prisms, but any shape with clear sharp corners may be affected.



Required Geometry


Required Objects

Kind Default Name Type Component Dimensions Description
Face Attribute Array FaceLabels int32_t (2) Specifies which Features are on either side of each Face
Feature Attribute Array Centroids float (3) Coordinates of the center of mass for a given enclosed Feature
Feature Attribute Array Volumes float (1) Volume of the enclosed Feature
Attribute Matrix FaceFeatureData Face Feature N/A Feature Attribute Matrix of the selected _Face Labels

Created Objects

Kind Default Name Type Component Dimensions Description
Feature Attribute Array AspectRatios float (2) Ratio of axis lengths (b/a and c/a) for best-fit ellipsoid to enclosed Feature
Feature Attribute Array AxisEulerAngles float (3) Euler angles (in radians) necessary to rotate the sample reference frame to the reference frame of the enclosed Feature, where the prinicpal axes of the best-fit ellipsoid are (X, Y, Z)
Feature Attribute Array AxisLengths float (3) Axis lengths (a, b, c) for best-fit ellipsoid to enclosed Feature
Feature Attribute Array Omega3s float (1) 3rd invariant of the second-order moment matrix for the enclosed Feature, does not assume a shape type (i.e., ellipsoid)

References ##

[1] Representation and Reconstruction of Three-dimensional Microstructures in Ni-based Superalloys, AFOSR FA9550-07-1-0179 Final Report, 20 Dec 2010.

[2] On the use of moment invariants for the automated classifcation of 3-D particle shapes, J. MacSleyne, J.P. Simmons and M. De Graef, Modeling and Simulations in Materials Science and Engineering, 16, 045008 (2008).

[3] n-Dimensional Moment Invariants and Conceptual Mathematical Theory of Recognition n-Dimensional Solids, Alexander G. Mamistvalov, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 8, AUGUST 1998, p. 819-831.

[4] M. Groeber, M. Uchic, D. Dimiduk, and S. Ghosh. A Framework for Automated Analysis and Simulation of 3D Polycrystalline Microstructures, Part 1: Statistical Characterization Acta Materialia, 56 (2008), 1257-1273.

Example Pipelines

Please see the description file distributed with this plugin.

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