Abstract
Stochastic textures with features spanning many length scales arise in a range
of contexts in physical and natural sciences, from nanostructures like synthetic bone to ocean wave height distributions and cosmic phenomena like inter-galactic cluster void distributions. Here we used a data set of 35 surface topographies, each of 2400 x 2400 pixels with spatial resolution between 4 micron and 7 micron per pixel, and fitted trivariate Gaussian distributions to represent their spatial structures. For these we computed pairwise information metric distances using the Fisher-Rao metric. Then dimensionality reduction was used to reveal the groupings among subsets of samples in an easily comprehended graphic in 3-space. The samples here came from the papermaking industry but such a reduction of large frequently noisy spatial data
sets is useful in a range of materials and contexts at all scales
of contexts in physical and natural sciences, from nanostructures like synthetic bone to ocean wave height distributions and cosmic phenomena like inter-galactic cluster void distributions. Here we used a data set of 35 surface topographies, each of 2400 x 2400 pixels with spatial resolution between 4 micron and 7 micron per pixel, and fitted trivariate Gaussian distributions to represent their spatial structures. For these we computed pairwise information metric distances using the Fisher-Rao metric. Then dimensionality reduction was used to reveal the groupings among subsets of samples in an easily comprehended graphic in 3-space. The samples here came from the papermaking industry but such a reduction of large frequently noisy spatial data
sets is useful in a range of materials and contexts at all scales
| Original language | English |
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| Title of host publication | Computational Information Geometry: For Image and Signal Processing |
| Publisher | Springer Nature |
| Publication status | Accepted/In press - 12 May 2016 |
Publication series
| Name | Information and Communications Technology |
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| Publisher | Springer |