An Image-Driven Mining Approach for Opacity Transfer Function
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 noncontributing 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.
Yunhai Wang Wei Chen Jian Zhang YanGang Wang Xuebin Chi
The SuperComputing Center Computer Network Information Center Chinese Academy of cience Beijing, Chi The State Key Lab of CAD&CG,Zhejiang University Hangzhou, China The SuperComputing Center Computer Network Information Center Chinese Academy of Science Beijing, Ch
国际会议
2010 4th International Universal Communication Symposium(第四届国际普遍交流学术研讨会 IUCS 2010)
北京
英文
233-241
2010-10-18（万方平台首次上网日期，不代表论文的发表时间）