SAVE: saliency-assisted volume exploration

Interactive visualization has become a valuable tool in visual exploration of scientific data. One prerequisite and fundamental issue is how to infer three-dimensional information through users’ two-dimensional input. Existing approaches commonly build on the hypothesis that user input is precise, which is sometimes invalid because of multiple causes like data noise, limited resolution of display devices and users’ casual input. In this paper, we reconsider some design choices of previous methods and propose an alternative effective algorithm for inferring interaction position in scientific data, especially volume data exploration. Our method automatically assists user interaction with the defined saliency. The presented saliency integrates data value, corresponding transfer function and user input. The result saliency implies remarkable regions of raw data as existing methods. Moreover, it reflects the areas of users’ concern. Thirdly, it eliminates the errors from data and device, helping users get the region they focus on. Various experiments have verified that our method can reasonably refine user interaction and effectively help users access interested features.