驾驭式可视化中成象技术的研究

In current research and practice, the data to be handled becomes greater and greater in size, and more and more complex in structure. To utilize the human's ability to improve the visualization efficiency on these data sets, navigation visualization allows scientists to wander in the data space, emphasizing on local interesting contents. Thus, the rendering complexity for visualization can be reduced greatly, and scientists can watch and analyze their interesting contents soon. In this project, we focused on studying the methods for fast extracting the data of interesting contents from a large and complex data set and rendering these data in high efficiency when the scientists wander in the data space. With the newest development for navigation visualization in the world, we developed many advanced methods that can quickly locate the scientists in the data space, take advantages of some coherent features with the viewpoint moving to grealy reduce the rendering complexity, and fast obtain the arbitrary selection of interesting contents and render them quickly. These methods greatly improve the rendering efficiency in navigation visualization.

针对驾驭式可视化连续观察局部数组的特点，研究其操作过程中所考察内容不断变化时的成象技术，以及人机交互反应的技术，主要探索数据场的组织结构和表达方式的优化，以有利于快速查找各种信息和利用已较成熟的技术（特别是硬件技术）加速成象。同时，研究数据内容与图象关联的新机制，减少人机交互反应的复杂度，以便用户快速得到所需的可视图。