134ボリュームデータマイニング

抄録

Volume visualization has served as an indispensable tool to explore the inner structures and complex behavior of volumetric objects embedded in large-scale sampled or simulated 3D datasets. However, the rapid increase in data size makes it difficult for us to sufficiently adjust visualization-related parameters for generating informative images. In order to compensate the lack of interactivity and provide the user with the serendipity, this keynote paper exploits the potential of a novel visualization concept, termed volume data mining.