CCRI: Planning: Development of a Community Resource for Digital Image Research

CCRI：规划：数字图像研究社区资源的开发

• 批准号: 1925494
• 负责人: Iyad Obeid
• 金额: $ 10万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925494&HistoricalAwards=false
• 关键词: CCRI; Planning; Development; Community; Resource

This project is concerned with research on very large digital images. Although image recognition tools are now commonplace for standard definition images, there is a lack of tools and techniques for analyzing very high resolution images (hundreds of millions of pixels per image). There are many important applications that would be well-served by the ability to automatically search such images, especially in cases where the properties of the search target aren't even fully known. Research of this nature is complicated by the fact that images of this size do not lend themselves well to the type of software tools that have been successful in standard-resolution image processing techniques such as handwriting or facial recognition. Data quantity alone is a major limitation; training a robust image recognition tool may require tens of thousands of images; at 100 million pixels per image, the sheer quantity of data becomes an issue for storing, sharing, and processing. New innovations will be required in machine learning, data provenance and warehousing, cloud computing, and image compression, all of which will serve the national interest.The specific purpose of this project is to (a) build a community of stakeholders who have vested interests in innovations in high resolution image processing and (b) create a roadmap for future research. Specifically, the researchers will reach out to relevant groups across academia, industry, and not-for-profit consortia with the goal of building a robust community with diverse expectations. This community will engage in a series of workshops to define the goals and scope of a high definition image processing consortium. The workshops will seek to define data standards, image processing goals, standardized data sets, and best practices for sharing and computing images at the petabyte scale. These findings will be leveraged into a future project whose goal will be to actually build the tools and perform the research that has been road-mapped by this project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目涉及超大型数字图像的研究。虽然图像识别工具现在对于标准清晰度图像是常见的，但是缺乏用于分析非常高分辨率图像（每个图像数亿像素）的工具和技术。有许多重要的应用程序，将很好地服务于自动搜索这样的图像的能力，特别是在搜索目标的属性甚至不完全知道的情况下。这种性质的研究是复杂的，因为这种大小的图像不适合在标准分辨率图像处理技术（如手写或面部识别）中取得成功的软件工具。数据量本身就是一个主要的限制;训练一个强大的图像识别工具可能需要数万张图像;在每张图像1亿像素的情况下，数据的绝对数量成为存储、共享和处理的问题。在机器学习、数据来源和仓储、云计算、图像压缩等领域都需要新的创新，所有这些都将服务于国家利益。本项目的具体目的是（a）建立一个对高分辨率图像处理创新有既得利益的利益相关者社区（B）为未来的研究制定路线图。具体来说，研究人员将接触学术界、工业界和非营利财团的相关团体，目标是建立一个具有不同期望的强大社区。该社区将参与一系列研讨会，以确定高清晰度图像处理联盟的目标和范围。这些讲习班将寻求确定数据标准、图像处理目标、标准化数据集以及在PB级共享和计算图像的最佳做法。这些发现将被用于未来的项目，其目标将是实际构建工具并执行该项目已制定的研究。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。