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Proceedings Paper

Geometric morphology of granular materials
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Paper Abstract

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 tessellation 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.

Paper Details

Date Published: 23 October 2000
PDF: 6 pages
Proc. SPIE 4117, Vision Geometry IX, (23 October 2000); doi: 10.1117/12.404821
Show Author Affiliations
Bernd R. Schlei, Los Alamos National Lab. (United States)
Lakshman Prasad, Los Alamos National Lab. (United States)
Alexei N. Skourikhine, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 4117:
Vision Geometry IX
Longin Jan Latecki; David M. Mount; Angela Y. Wu, Editor(s)

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