We present a new method to transform the spectral pixel information of a micrograph into an affine geometric description, which allows us to analyze the morphology of granular materials. We use spectral and pulse-coupled neural network based segmentation techniques to generate blobs, and a newly developed algorithm to extract dilated contours. A constrained Delaunay tesselation of the contour points results in a triangular mesh. This mesh is the basic ingredient of the Chodal Axis Transform, which provides a morphological decomposition of shapes. Such decomposition allows for grain separation and the efficient computation of the statistical features of granular materials.
@article{arxiv.cs/0006047,
title = {Geometric Morphology of Granular Materials},
author = {B. R. Schlei and L. Prasad and A. N. Skourikhine},
journal= {arXiv preprint arXiv:cs/0006047},
year = {2015}
}
Comments
6 pages, 9 figures. For more information visit http://www.nis.lanl.gov/~bschlei/labvis/index.html