Geometric Morphology of Granular Materials

TL;DR

This paper introduces a novel geometric analysis method for granular materials using spectral segmentation, contour extraction, and a shape decomposition technique to analyze morphology and compute statistical features.

Contribution

It presents a new approach combining spectral analysis, neural network segmentation, and a shape decomposition algorithm for granular material morphology analysis.

Findings

Effective segmentation of micrograph images into granular blobs.

Accurate morphological decomposition enabling statistical analysis.

Enhanced understanding of granular material structures.

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 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.