Unsupervised segmentation of carbonate thin section images. (Deliverable D2.1 - Dataset)

Unsupervised segmentation of carbonate thin-section images

This data repository is part of Deliverable 2.1 of the Horizon-EU project GO-Forward. It comprises data and processing code for automated mineral phase identification and porosity estimation from thin sections using superpixel segmentation and unsupervised clustering algorithms.

The segmentation pipeline uses a two-stage unsupervised approach:

1. SLIC superpixel segmentation: Over-segments each image into ~3000   compact, color-homogeneous regions.2. K-Means clustering in CIELAB color space: Clusters superpixels   globally (across all images) into 3 classes based on mean L*, a*, b*   and grayscale intensity.

Porespace is automatically identified by its distinctive blue-dye signature(negative a* and b* values in CIELAB space).

Code is licensed under MIT.

Data is licensed under CC BY.

More information is provided in the README.md