Automatic particle analysis for microgeology

Automatic particle analysis for microgeology

Software

This software implements methods for automated recognition of particles in images from scanning electron microscope (SEM). The input is image of backscattered electrons (BSE), the output is per-pixel labels identifying individual particles. This task is hard to solve with current methods since many particles are often perceived as one connected area. Two methods are implemented. The first analyzes contour of an area and employs mechanical rules in order to propose points where compound particles break. The second method uses neural networks to determine boundary pixels of the individual particles.

This software implements methods for automated recognition of particles in images from scanning electron microscope (SEM). The input is image of backscattered electrons (BSE), the output is per-pixel labels identifying individual particles. This task is hard to solve with current methods since many particles are often perceived as one connected area. Two methods are implemented. The first analyzes contour of an area and employs mechanical rules in order to propose points where compound particles break. The second method uses neural networks to determine boundary pixels of the individual particles.

microgeology, SEM, BSE, particle, segmentation, neural networks, deep learning

microgeology, SEM, BSE, particle, segmentation, neural networks, deep learning

(Není veřejně dostupné, jedná se o výsledek smluvního výzkumu.)

K využití výsledku jiným subjektem je vždy nutné nabytí licence

https://www.fit.vut.cz/research/product/768/