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

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.

本数据集的辅助材料包括图表和表格，详细阐述了在图像建模中应用水平集的使用情况，具体见于题为《提升空间图像分析：水平集在建模视角下的应用价值》的论文中。特别指出，水平集在图像噪声去除方面的应用，以及对现有自适应中值平滑技术的增强。随后，水平集被用于将图像表示为图模型。这些图模型表示随后被用于训练健康患者和哮喘患者纤维蛋白网络的模型图像，以便进行推理。最后，展示了如何利用水平集来改进名为D-RISE的技术，以提升深度学习模型的可解释性。