体数据分类是体绘制中传递函数设计的核心问题.标量值-梯度模直方图作为表征体数据的一种经典二维特征空间,已被广泛应用于分类体数据.然而,大部分已有方法存在过于依赖分类算法的参数设置、运算效率低、交互复杂度高等问题.以标量值-梯度模直方图的密度分布为基础,并依据物质中心密度大且物质中心间距离远这一特性,首先快速计算每个数据点的密度及每个数据点到比其密度大的点的最小距离;然后,将所有数据点投影到密度-距离图,并以密度-距离图作为人机接口,使用户能够交互地选择多个密度中心来分类体数据并设置传递函数.通过多组实验验证,所提出的方法无需预设物质类别的数量,分割标量值-梯度模直方图的准确度较高且速度较快,所设计的密度-距离图是一个有效的人机交互接口,可以有效地引导用户完成由粗糙到精细的递进式体数据分类和可视化过程.%Volume data classification is a core issue of transfer function in volume rendering. Scalar-gradient magnitude histogram of volume is a classic feature space, and has been applied in volume classification for its nice result in visual extraction of boundaries between different materials. However, the design of transfer function based on scalar-gradient histogram has proven as a time-consuming and complex task which is hard for users to conduct interactions. In this paper, scalar-gradient histogram is treated as a density distribution of all voxels. This approach assumes that the density of a material center is higher than their neighbors and the distance between two material centers is far enough. By computing the minimum distance between each points and all other points with higher density in scalar-gradient histogram, a density-distance graph is constructed based on densities and minimum distances of all points. The density peaks are easily observed in the graph and can guide the users to select centers of each material as a progressive volume classification process through a set of specified interactions. Experimental results demonstrate that the presented approach does not require the prior knowledge of categories, and the volume classification is accurate with high performance.
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