Collaborative Proposal: ABI Innovation: Rapid, Interactive, Visual Mining of Biological Motion

合作提案：ABI 创新：生物运动的快速、交互式、可视化挖掘

• 批准号: 1262472
• 负责人: Min Shin
• 金额: $ 74.34万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262472&HistoricalAwards=false
• 关键词: Collaborative; Proposal; ABI; Innovation; Rapid

Across the biological sciences, high-quality, high frame-rate images and video are widely used in analysis of sub-cellular organelles to individual organisms. The use of video has become so pervasive, that many research teams accumulate much more data than can be reasonably analyzed by current methods. This project will develop algorithms to allow the rapid and affordable mining of very large video sets for motion behaviors of interest in the study of the collective behavior and emergent properties of complex systems of organisms. This work is motivated by the needs of domain experts in various areas of biological motion analysis and incorporates research in computer vision, image processing, user interfaces, and visualization. A key part of the project is a fast, semi-automated biological object tracker that leverages the computational power of off-the-shelf hardware and incorporates simple user interactions to dramatically improve tracking accuracy in the types challenging cases that frequently arise in biological image analysis. Domain experts will be able to search motion databases through visual query by selecting an example motion of interest. The system will be evaluated on three biological research applications: honeybee behavior, ant colony networks, and cell motion analysis. Groups of organisms can display a rich and sophisticated behavioral repertoire and are able to make complex collective decisions. Studying these interactions provides insight into collective decision-making, adaptive networks, and division-of-labor. Understanding these complex interactions over large time scales requires new methods of data analysis that this project will address with an interdisciplinary collaboration between biologists and computer scientists. The outreach components of this work include demonstrations and explanations of the complex behavior of groups of organisms and how these behaviors can be studied using computing. The system is intended to be widely-applicable and will be disseminated to other researchers by making the software available for download online.

在整个生物科学中，高质量、高帧率的图像和视频被广泛用于分析个体生物体的亚细胞器。视频的使用已经变得如此普遍，以至于许多研究团队积累的数据远远超过了当前方法可以合理分析的数据。该项目将开发算法，以允许快速和负担得起的挖掘非常大的视频集的运动行为的兴趣在研究的集体行为和紧急性质的复杂系统的有机体。这项工作的动机是领域专家在生物运动分析的各个领域的需求，并结合了计算机视觉，图像处理，用户界面和可视化的研究。该项目的一个关键部分是一个快速的半自动生物对象跟踪器，它利用了现成硬件的计算能力，并结合了简单的用户交互，以显着提高生物图像分析中经常出现的具有挑战性的情况下的跟踪精度。领域专家将能够通过选择感兴趣的示例运动来通过视觉查询搜索运动数据库。该系统将评估三个生物研究应用：蜜蜂行为，蚁群网络和细胞运动分析。生物群体可以表现出丰富而复杂的行为，并能够做出复杂的集体决策。研究这些相互作用可以深入了解集体决策，适应性网络和劳动分工。了解这些复杂的相互作用，在大的时间尺度需要新的数据分析方法，该项目将解决与生物学家和计算机科学家之间的跨学科合作。这项工作的外展部分包括演示和解释生物群体的复杂行为，以及如何使用计算来研究这些行为。 该系统打算广泛适用，并将通过提供在线下载软件的方式向其他研究人员传播。