Semi-automatic segmentation of petrographic thin section images using a "seeded-region growing algorithm" with an application to characterize wheathered subarkose sandstone
Semi-automatic segmentation of petrographic thin section images using a "seeded-region growing algorithm" with an application to characterize wheathered subarkose sandstone
P. Asmussen,O. Conrad,2 Autori,U. Riller
2015 · DOI: 10.1016/j.cageo.2015.05.001
Computational Geosciences · 59 citazioni
TLDR
A new semi-automatic image segmentation workflow for the quantitative analysis of microscopic grain fabrics is presented, which uses an automated seeded region growing algorithm, which is based on variance analysis of five or more RGB images.