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Data · dataset · 2022

Generating a pixel-wise annotated training dataset to train ML algorithms for mineral identification in Mineral thin sections

Listed in Teesside University Research Data Repository

The dataset contains two folders. 1.

Description

In the "virtual_microscopy" folder, you can find a short screen recording showing how PetroScan works. PetroScan is the built-in toolbox of Virtual Petrography (ViP) platform used for displaying digital thin section images under different modes.

The video records the extinction behavior and the change of inference color of quartz and mica in a mylonite sample under cross-polarized when moving the angle selection wheel. 2. "Superpixel_evaluation_example" contains boundary maps and raw images of a subarea of Bentheimer Sandstone. The superpixel segmentation results using different superpixel algorithms can be found in the subfolder "qualitative_evaluation". 3. "Bentheimer" contains xpol and ppol images of the sample used in the paper.

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Where it is published

Catalogue records · 1

Topics

Inferred from text
Image 65% · Video 75%
Provenance · 1 source records, 8 field assertions
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Teesside University Research Data Repositoryoai:data.mendeley.com/cp897djgn9.28 h agoJSON v1
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concepts[modality].local:modality:videoenrichment · researchdata tees ac ukkeyword-concept-rules@1.0.0title+description (75%)
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