Boundary identification in EBSD data with a generalization of fast multiscale clustering.

Electron backscatter diffraction (EBSD) studies of cellular or subgrain microstructures present problems beyond those in the study of coarse-grained polycrystalline aggregates. In particular, identification of boundaries delineating some subgrain structures, such as microbands, cannot be accomplished simply with pixel-to-pixel misorientation thresholding because many of the boundaries are gradual transitions in crystallographic orientation. Fast multiscale clustering (FMC) is an established data segmentation technique that is combined here with quaternion representation of orientation to segment EBSD data with gradual transitions. This implementation of FMC addresses a common problem with segmentation algorithms, handling data sets with both high and low magnitude boundaries, by using a novel distance function that is a modification of Mahalanobis distance. It accommodates data representations, such as quaternions, whose features are not necessarily linearly correlated but have known distance functions. To maintain the linear run time of FMC with such data, the method requires a novel variance update rule. Although FMC was originally an algorithm for two-dimensional data segmentation, it can be generalized to analyze three-dimensional data sets. As examples, several segmentations of quaternion EBSD data sets are presented.

[1]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[2]  Michael A. Groeber,et al.  Deriving grain boundary character distributions and relative grain boundary energies from three-dimensional EBSD data , 2010 .

[3]  H. Schaeben,et al.  Texture Analysis with MTEX – Free and Open Source Software Toolbox , 2010 .

[4]  P. Mahalanobis On the generalized distance in statistics , 1936 .

[5]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[6]  Lori Bassman,et al.  Three-dimensional morphology of microbands in a cold-rolled steel , 2007 .

[7]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[8]  Ronen Basri,et al.  Fast multiscale image segmentation , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[9]  F. J. Humphreys,et al.  Three-dimensional investigation of particle-stimulated nucleation in a nickel alloy , 2007 .

[10]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[11]  Zhaohui Huang,et al.  Efficient Algebraic Multigrid Algorithms and Their Convergence , 2002, SIAM J. Sci. Comput..

[12]  G. Golub,et al.  Updating formulae and a pairwise algorithm for computing sample variances , 1979 .

[13]  Michael T. Heath,et al.  Scientific Computing: An Introductory Survey , 1996 .

[14]  E. Ovtchinnikov Cluster robustness of preconditioned gradient subspace iteration eigensolvers , 2006 .

[15]  F. J. Humphreys,et al.  Orientation averaging of electron backscattered diffraction data , 2001, Journal of microscopy.

[16]  A. Rollett,et al.  Three-Dimensional Characterization of Microstructure by Electron Back-Scatter Diffraction , 2007 .

[17]  Ronen Basri,et al.  Texture segmentation by multiscale aggregation of filter responses and shape elements , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[18]  Pietro Perona,et al.  Self-Tuning Spectral Clustering , 2004, NIPS.

[19]  F. Flores,et al.  Interfaces in crystalline materials , 1994, Thin Film Physics and Applications.

[20]  T. Velmurugan,et al.  A Survey of Partition based Clustering Algorithms in Data Mining: An Experimental Approach , 2011 .

[21]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[22]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  R. Basri,et al.  Fast Multilevel Clustering , 2005 .

[24]  W. Hamilton ON A NEW SPECIES OF IMAGINARY QUANTITIES CONNECTED WITH A THEORY OF QUATERNIONS By William Rowan Hamilton , 1999 .

[25]  Lori Bassman,et al.  The three-dimensional nature of microbands in a channel die compressed Goss-oriented Ni single crystal , 2011 .

[26]  Achi Brandt,et al.  Fast multiscale clustering and manifold identification , 2006, Pattern Recognit..

[27]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[28]  Catherine A. Sugar,et al.  Empirically defined health states for depression from the SF-12. , 1998, Health services research.