University of Pretoria
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Reason: The content contained within this dataset is derived from the PhD dissertation 'Enhancing spatial image analysis: modelling perspectives on the usefulness of level-sets'. The dissertation has an embargo of two years to allow the research team to complete negotiations regarding publication or patents. By association, this dataset inherits the same embargo period as the PhD dissertation.

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Advancing image modelling using level-sets : simulation, convergence, practical inference analysis

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posted on 2024-03-11, 10:40 authored by Jean-Pierre StanderJean-Pierre Stander

The supporting material includes figures and tables detailing the use of level-sets within image modelling in the thesis titled 'Enhancing spatial image analysis: modelling perspectives on the usefulness of level-sets'. Specifically the use of level-sets for noise removal in images, enhancing the existing Adaptive Median Smoother technique. Level-sets are then used to represent images as graphical models. These graphical model representation are then used to train model image of the fibrin networks of healthy and asthmatic patients to allow for inference to be drawn. Lastly, it is shown how level-sets can be used to improve a technique called D-RISE for improved explainability of deep learning models.

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Statistics

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