Semi-automatic segmentation of petrographic thin section images using a seeded-region growing algorithm with an application to characterize wheathered subarkose sandstone
We present a new semi-automatic image segmentation workflow for the quantitative analysis of microscopic grain fabrics. The workflow uses an automated seeded region growing algorithm. The workflow is implemented in the open-source Geographic Information System (GIS) software SAGA.
thin section images seeded region growing geographic information system (gis) sedimentary rocks fabric analysis grain object detection