体绘制中的自动转换函数研究与实现

Transfer Functions Design for Volume Rendering by Using Neural Networks

转换函数是体绘制一个必不可少的组成部分。针对传统的自动转换函数缺乏通用性和交互性的不足,提出一种通用自动转换函数GATF,基于自组织映射神经网络SOM识别数据集的类型,利用反向传播神经网络BPN得到相应转换函数的权值,相应的类型和权值信息保存于信息库中,这样任何数据集只要能在信息库中找到相同或相似度足够的类型,就可以直接取出BPN权值自动进行颜色转换。实验结果证实GATF达到了转换函数的通用性,具有类型识别率高和更能揭示细节的能力。

The design of transfer functions is a key process in volume visualization applications. However, traditional transfer function design lacks flexibility and interaction. Moreover, it is difficult and time-consuming for the users to design new proper transfer function when the types of the studied data sets change. By introducing neural networks into the transfer function design, a general automatic transfer function (GATF) was proposed. Experimental results show that by using neural networks to guide the transfer function design, the robustness of volume rendering is promoted and the corresponding classification process is optimized.