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

III-CXT-Large：在探索科学图像时使用不确定数据

• 批准号: 0808772
• 负责人: Bangalore Manjunath
• 金额: $ 299.97万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0808772&HistoricalAwards=false
• 关键词: III; CXT; Large; Working; Uncertain

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.

不确定性要素是对科学和工程应用的复杂图像数据的管理和分析所固有的。 这项工作以先前的多学科工作为基础，用于存储，管理和分析脊椎动物中央神经系统和亚细胞环境中细胞体系结构的生物图像，但是这些技术是一般的，并且针对其他领域，例如环境管理，地理信息科学，遥感和交互式数字多媒体。成像是在许多科学发现的核心处，其信息是根据原始像素强度和多种颜色或高光谱图像的多种渠道捕获的信息。这项工作包括生成概率测量值和图像分析方法的量化不确定性，模式分类方法生成可以作为概率特征表存储的信息以及可视化概率信息的新方法。 拟议的工作将集成到UCSB生物图像搜索和查询环境中，校园数据基础架构的一部分，开发的软件将作为开源。