摘要：为了增强传递函数的特征区分能力并简化交互模式,提出一种基于二维直方图图像分割和特征度量差异性分析的渐进式体绘制传递函数设计方法.通过渐进式地用不同的特征组成传递函数对目标区域进行逐步求精地分离,并在每次分离时采用面向数据分类的特征空间度量差异性的评估方法智能指导二维传递函数数值特征的选取.使用这一系列二维传递函数对目标区域进行多次分离,得到目标区域精确的分离结果;再利用此结果突出目标区域的显示,增强可视化效果.由于该方法借鉴了前人建立在对二维直方图图像进行normalized cut分割和层次聚类的交互方式,使得二维直方图的交互也非常简单便捷.实验结果表明,文中方法是有效的.

摘要：Numerical weather simulation data usually comprises various meteorological variables, such as precipitation, temperature and pressure. In practical applications, data generated with several different numerical simulation models are usually used together by forecasters to generate the final forecast. However, it is difficult for forecasters to obtain a clear view of all the data due to its complexity. This has been a great limitation for domain experts to take advantage of all the data in their routine work. In order to help explore the multi-variate and multi-model data, we propose a stamp based exploration framework to assist domain experts in analyzing the data. The framework is used to assist domain experts in detecting the bias patterns between numerical simulation data and observation data. The exploration pipeline originates from a single meteorological variable and extends to multiple variables under the guidance of a designed stamp board. Regional data patterns can be detected by analyzing distinctive stamps on the board or generating extending stamps using the Boolean set operations. Experiment results show that some meteorological phenomena and regional data patterns can be easily detected through the exploration. These can help domain experts conduct the data analysis efficiently and further guide forecasters in producing reliable weather forecast.