the grain boundary criterion of fast multiscale clustering; MATLAB’s gbcFMC

Syntax

crit = gbcFMC()

The default parameters.

crit = gbcFMC(3.8, quatmax=5*degree)

More grains, a tighter outlier radius.

grains = calcGrains(ebsd, criterion=gbcFMC(3.8, minPixel=5))

As the criterion of calcGrains.

grains = calcGrains(ebsd, fmc=3.8)

The same by the keyword of calcGrains.

Description

Fast multiscale clustering (McMahon et al.) coarsens the graph of neighbouring pixels into a hierarchy of nested aggregates, each with its mean orientation and orientation spread. Every pixel is read off at the scale where its aggregate stands out most.

There is no threshold angle. The coupling of two aggregates is measured against their own spread, after what the lattice gradient of each explains, so a small misorientation separates two uniform aggregates and does not separate the parts of a deformed grain. cmaha is the main parameter: larger values give more grains.

Each indexed phase is clustered on its own; a pair of pixels of two phases is always a boundary.

Keywords

keyword	default	description
`cmaha`		the sharpness of the rebias of the coarse couplings by the aggregates’ spread; larger gives more grains
`cmaha0`		the decay of the initial coupling of two pixels per degree of misorientation
`quatmax`		the largest outlier radius of an aggregate, an angle
`alpha`		how readily a node becomes a seed of the coarse level: the share of its coupling that must reach the seeds
`gammaW`		the edge dilution: a coupling weaker than the average one of both its nodes divided by gammaW is dropped
`maxDelta`		the misorientation angle above which two pixels are never coupled
`minPixel`		the smallest region left standing; smaller ones join their neighbours
`verbose`		print the hierarchy of every phase

Output

crit gbcFMC, a grainBoundaryCriterion

References

C. McMahon, B. Soe, A. Loeb, A. Vemulkar, M. Ferry, L. Bassman, Boundary identification in EBSD data with a generalization of fast multiscale clustering, Ultramicroscopy 133 (2013) 16-25

Members

clusterPhase	FMC on the pixels isP of one phase over the pair list (ip, jp) within them
evaluate	1 for every pair of pixels i, j that FMC puts into one cluster, 0 otherwise, each indexed phase clustered on its own
gbcFMC	the grain boundary criterion of fast multiscale clustering
setMinPixel	the same criterion with another smallest region left standing

MATLAB’s gbcFMC_index

source

Citing this page. This page is part of the documentation of MTEX, a free and open source MATLAB toolbox for analyzing and modeling crystallographic textures. It was written by The MTEX Developers and is published at https://mtex-toolbox.github.io/gbcFMC_index_py.html. If you use MTEX, or reuse text or figures from this page, in your research, please cite

F. Bachmann, R. Hielscher, H. Schaeben: Texture Analysis with MTEX - Free and Open Source Software Toolbox, Solid State Phenomena 160 (2010), 63-68. 10.4028/www.scientific.net/SSP.160.63

BibTeX

@article{bachmann2010mtex,
  author  = {F. Bachmann and R. Hielscher and H. Schaeben},
  title   = {Texture Analysis with MTEX - Free and Open Source Software Toolbox},
  journal = {Solid State Phenomena},
  volume  = {160},
  pages   = {63-68},
  year    = {2010},
  doi     = {10.4028/www.scientific.net/SSP.160.63},
  url     = {https://doi.org/10.4028/www.scientific.net/SSP.160.63}
}

Other papers describing specific MTEX methods are listed under Publications — please cite the one that best fits your application. The MTEX source code is licensed under the GNU General Public License v2.0; the text and figures of this documentation are licensed under CC BY 4.0, which permits reuse — including by automated systems — provided The MTEX Developers and this page are credited.

The class gbcFMC