Uncertainty can be defined as error (outlier or deviation from a true value), imprecision (resolution of a value compared to the needed resolution), subjectivity (degree of subjective influence in the data), and non-specificity (lack of distinction for objects). Uncertainty is everywhere around us and when analyzing data with inherent uncertainty, the whole analytical process may be affected. It is important to investigate and understand how uncertainty and its different types can influence the analytical process and its outcomes.
- Conduct a thorough literature review and categorize uncertainty types encountered in analytical processes (e.g., noise or errors in the acquisition of the data, different alternatives in the filtering of the data, or mapping and rendering choices).
- Design a study to investigate the impact of different uncertainties in the analytical process and its outcomes.
- Quantify and visualize the uncertainty impact throughout the analytical process.
- Interest and knowledge in image processing, statistics, and (medical) visualization.
- Good programming skills.
- Creativity and enthusiasm.
To be discussed (depending on the background of the student).