CAREER: High-order Tensor Analysis for Groupwise Correspondence: Theory, Algorithms, and Applications

职业：分组对应的高阶张量分析：理论、算法和应用

• 批准号: 1350521
• 负责人: Haibin Ling
• 金额: $ 47.97万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-01 至 2019-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1350521&HistoricalAwards=false
• 关键词: CAREER; order; Tensor; Analysis; Groupwise

Visual matching is a fundamental problem in computer vision (CV) and intensive research efforts have been devoted to building correspondence between a pair of visual objects. By contrast, finding correspondence among an ensemble of objects remains challenging. This project develops a unified framework for this problem and to apply the framework to different applications. The research establishes a close correlation between the classical multi-dimensional assignment (MDA) problem and low-rank tensor approximation. Such correlation paves a way of using high-order tensor analysis for groupwise visual matching that assumes an MDA formulation. Along the way, a series of algorithms are developed to address challenging issues such as computational efficiency and context modeling. These algorithms are then deployed to different tasks including simultaneous tracking of multiple targets, tracking of deformable structures, and batch alignment of visual ensembles. This project can generate broad impact on areas of computer vision, computer graphics, combinatorial optimization, oral and maxillofacial radiology, image-guided intervention, physical therapy, security and defense, education research, etc. On the one hand, the fundamental importance of visual matching makes the project transformative to many other CV problems. On the other hand, the project benefits a wide range of fields outside the CV community through the use of interdisciplinary applications as test beds. This project also integrates tightly research and education with highlights on supervising students from underrepresented groups, combining computer vision and education research, and involving undergraduates in research.

视觉匹配是计算机视觉（CV）中的一个基本问题，密集研究工作已致力于在一对视觉对象之间建立对应关系。相比之下，在物体集合之间找到对应关系仍然具有挑战性。该项目为此问题开发了一个统一的框架，并将框架应用于不同的应用程序。该研究建立了经典多维分配（MDA）问题与低级张量近似之间的密切相关性。这种相关性铺平了一种使用高阶张量分析来假定MDA公式的群体视觉匹配的方法。一路上，开发了一系列算法来解决诸如计算效率和上下文建模等具有挑战性的问题。然后将这些算法部署到不同的任务中，包括对多个目标的同时跟踪，跟踪可变形结构以及视觉合奏的批处理。该项目可以对计算机视觉，计算机图形，组合优化，口腔和上颌面放射学，图像引导的干预，物理疗法，安全和防御，教育研究等领域产生广泛的影响。一方面，视觉匹配的基本重要性使得该项目转化为许多其他CV问题。另一方面，通过使用跨学科应用程序作为测试床，该项目使CV社区以外的广泛领域受益。该项目还将严格的研究和教育与监督代表性不足的团体的监督学生，将计算机视觉和教育研究结合在一起，并参与研究的重点。