EAGER: A New Framework for Balancing Deformability and Discriminability in Computer Vision

EAGER：平衡计算机视觉中的可变形性和可辨别性的新框架

• 批准号: 1049032
• 负责人: Haibin Ling
• 金额: $ 6.89万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1049032&HistoricalAwards=false
• 关键词: EAGER; New; Framework; Balancing; Deformability

Deformability and discriminability are often two "conflicting" factors in computer vision problems such as shape matching and object recognition. For example, it has been observed that strong deformation invariant descriptors often suffer from low discriminative powers for category recognition. This EAGER project explores a new framework for balancing deformability and discriminability for computer vision tasks. The framework uniformly embeds an object, which can be a 2D shape, a point set, an image, a 3D volume or a surface, in a high dimensional space named aspect space. The embedding parameter is then used to control the degree of deformation insensitivity. Both the theoretic and application sides of the proposed framework are investigated. Based on the framework, the project aims to develop three additional research goals: robust shape matching methods by selecting deformability adaptively, robust point set registration methods by dealing with articulation in the framework, and robust image matching by extracting features in the embedded aspect space. These goals are planned to be evaluated on real applications including silhouette-based foliage data retrieval, 3D marker matching in computer-based physical therapy, and image-based disease screening. The project aims to bridge the two main problems, handling deformation and improving discriminability, which relate to many subfields inside and outside computer vision. The interdisciplinary applications are expected to generate significant contributions to various fields including biodiversity studies, biomedical study, etc. The research results, including code and data, are made public available through the project website.

可变形性和可区分性通常是计算机视觉问题中的两个“冲突”因素，例如形状匹配和对象识别。例如，已经观察到，强烈的变形不变描述符通常会遭受类别识别的歧视能力低。这个渴望的项目探索了一个新的框架，用于平衡计算机视觉任务的可区分性和可区分性。该框架均匀地嵌入了一个对象，该对象可以是2D形，一个点集，图像，3D体积或表面，在名为“远距离空间”的高维空间中。然后使用嵌入参数来控制变形不敏感的程度。研究了拟议框架的理论和应用方面。基于框架，该项目旨在通过在框架中处理表达方式来适应性，可靠的点设置注册方法来制定三个其他研究目标：可靠的形状匹配方法，以及通过在嵌入式的Exack Space中提取功能来匹配鲁棒的图像匹配。计划对这些目标进行评估，包括基于轮廓的叶子数据检索，基于计算机的物理疗法中的3D标记匹配以及基于图像的疾病筛查。该项目旨在弥合两个主要问题，即处理变形和提高可区分性，这与计算机内部和外部视觉范围内的许多子场有关。跨学科应用程序有望为包括生物多样性研究，生物医学研究等各个领域做出重大贡献。包括代码和数据在内的研究结果通过项目网站公开。