Sequential data visualization

In direct volume rendering, specifying appropriate transfer functions to efficiently classify volumetric data is a challenging task. One of the main reasons is the lack of a feedback mechanism to indicate which parts of the specified transfer function actually contribute to the resulting image at the given viewpoint. In this paper we propose a novel image-driven mining approach that can compute the minimum set of the components in the opacity transfer function which produces the rendered image. The mining is performed by culling the non-contributing parts with an image-difference constrained minimization process. By iteratively mining a specified opacity transfer function, a set of layered features in the volumetric datasets is sequentially generated. This enables a set of challenging visualization tasks, such as informative transfer function design, layer-based volume rendering, as well as automatic volume classification.