GuideME: slice-guided semiautomatic multivariate exploration of volumes

摘要: Multivariate volume visualization is important for many applications including petroleum exploration and medicine. State-of-the-art tools allow users to interactively explore volumes with multiple linked parameter-space views. However, interactions in the parameter space using trial-and-error may be unintuitive time consuming. Furthermore, switching between different views distracting. In this paper, we propose GuideME: a novel slice-guided semiautomatic multivariate approach. Specifically, approach comprises four stages: attribute inspection, guided uncertainty-aware lasso creation, automated feature extraction optional spatial fine tuning visualization. Throughout process, user does not need interact at all examples of complex real-world data demonstrate usefulness, efficiency ease-of-use our method.