NEURAL NETWORK-ENHANCED STEREOSCOPIC VISUALIZATION

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 映射到低维可视化的大型和高维数据集通常会导致感知模糊。一种这样的模糊性是重叠或遮挡，其发生在记录的数量超过可视化中的唯一位置的数量时，或者当存在映射到相同位置的两个或更多个记录时。为了减少遮挡的影响，应用非标准视觉属性（即阴影和/或透明度），或者可以将这些记录重新映射到相应的随机生成的抖动位置。虽然所得到的映射有效地描绘了记录的密度，但它也未能提供对相邻记录之间的关系的洞察。 我们的算法通过集成神经网络和可视化技术来解决这些问题，根据它们的维度值来替换记录，而不是更常见的记录随机位移。不幸的是，大型数据集和详细的可视化使这些算法的计算强度。为了解决这个问题，我们扩展的算法，以提供支持的计算环境，包括大量的集群计算机。我们使用针对任务分析、可用性评估和使用分析的经验评估来衡量新可视化的有效性，而算法的有效性则是在处理数据所需的时间内衡量的。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Large and high-dimensional data sets mapped to low-dimensional visualizations often result in perceptual ambiguities. One such ambiguity is overlap or occlusion that occurs when the number of records exceeds the number of unique locations in visualization or when there exist two or more records that map to the same location. To lessen the affect of occlusion, nonstandard visual attributes (i.e. shading and/or transparency) are applied, or such records may be remapped to a corresponding randomly-generated jittered location. While the resulting mapping efficiently portrays the density of records it also fails to provide the insight into the relationship between the neighboring records. Our algorithms address such issues through the integration of neural-network and visualization techniques, displacing records based on their dimensional values, instead of the more common random displacement of records. Unfortunately, large data sets and detailed visualizations make these algorithms computationally intense. To address this, we extend the algorithms to provide support for computational environments consisting of a large number of clustered computers. We measure the effectiveness of new visualizations using empirical evaluation targeted at task analysis, usability evaluation and usage analysis while the effectiveness of algorithms is measured in time required to process the data.