Transfer function optimization based on a combined model of visibility and saliency
Shengzhou Luo, John Dingliana · 2017
In this paper we present an automated approach for optimizing the conspicuity of features in 3D volume visualization. By iteratively adjusting the opacity transfer function, we are able to generate visualizations that satisfy a user-specified target distribution defining the relative conspicuity of particular features in the data set. Our approach exploits a metric, called Visibility-Weighted Saliency (VWS), that takes into account both the issues of view-dependent occlusion and visual saliency in defining the visibility of features in volume data. A parallel line search strategy is presented to improve the performance of the optimization mechanism. We demonstrate that the approach is able to achieve promising results in optimizing visualizations of both static and time-varying volume data.