Feature-preserving volume data reduction and focus+context visualization

Yu-Shuen Wang*, Chaoli Wang, Tong Yee Lee, Kwan Liu Ma


研究成果: Article同行評審

48 引文 斯高帕斯(Scopus)


The growing sizes of volumetric data sets pose a great challenge for interactive visualization. In this paper, we present a feature-preserving data reduction and focus+context visualization method based on transfer function driven, continuous voxel repositioning and resampling techniques. Rendering reduced data can enhance interactivity. Focus+context visualization can show details of selected features in context on display devices with limited resolution. Our method utilizes the input transfer function to assign importance values to regularly partitioned regions of the volume data. According to user interaction, it can then magnify regions corresponding to the features of interest while compressing the rest by deforming the 3D mesh. The level of data reduction achieved is significant enough to improve overall efficiency. By using continuous deformation, our method avoids the need to smooth the transition between low and high-resolution regions as often required by multiresolution methods. Furthermore, it is particularly attractive for focus+context visualization of multiple features. We demonstrate the effectiveness and efficiency of our method with several volume data sets from medical applications and scientific simulations.

頁(從 - 到)171-181
期刊IEEE Transactions on Visualization and Computer Graphics
出版狀態Published - 2011


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