III-CXT-Large: Working with Uncertain Data in Exploring Scientific Images

对于科学和工程应用的复杂图像数据的管理和分析来说，不确定因素是固有的。这项工作建立在以前多学科工作的基础上，用于存储、管理和分析脊椎动物中枢神经系统和亚细胞环境中细胞结构的生物图像，但这些技术是通用的，针对其他领域，如环境管理、地理信息科学、遥感和交互式数字多媒体。成像是许多科学发现的核心，通过原始像素强度和彩色或高光谱图像的多个通道捕获信息。这项工作包括从图像分析方法生成概率测量和量化的不确定性，生成可存储为概率特征表的信息的模式分类方法，以及显示概率信息的新方法。拟议的工作将纳入UCSB生物图像搜索和查询环境，这是校园数据基础设施的一部分，开发的软件将作为开放源码提供。

Elements of uncertainty are inherent to management and analysis of complex image data for scientific and engineering applications. The work builds on previous multidisciplinary work for storage, management, and analysis of biological images of cellular architectures in the vertebrate central nervous system and sub-cellular environments, but the techniques are general and target other areas, such as environmental management, geographical information science, remote sensing and interactive digital multimedia. Imaging is at the cores of many scientific discoveries, with information captured in terms of raw pixel intensities and in multiple channels for color or hyperspectral imagery. The work includes generation of probabilistic measurements and quantified uncertainties from image analysis methods, pattern classification methods generating information that can be stored as probabilistic feature tables and new approaches to visualization of probabilistic information. The proposed work will be integrated within the UCSB BioImage Search and Query environment, part of the campus data infrastructure, and the software developed will be made available as open source.