2026-10-15T06:30:19Z https://api.figshare.com/v2/oai

oai:figshare.com:article/25323946 2024-03-11T10:40:14Z category_29869 category_29887 category_29890 portal_753 item_type_3 month_year_03_2024

Advancing image modelling using level-sets : simulation, convergence, practical inference analysis Jean-Pierre Stander (18083413) Applied statistics Spatial statistics Statistical data science Graphic models Image modelling Level-sets Noise removal Adaptive median filter (AMF) Deep learning models Spatial image Adaptive median smoother technique SDG 4: Quality education <p>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.</p> 2024-03-11T10:40:14Z Dataset Dataset 10.25403/UPresearchdata.25323946.v1 https://figshare.com/articles/dataset/Advancing_image_modelling_using_level-sets_simulation_convergence_practical_inference_analysis/25323946 CC BY 4.0