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.

可变形性和可辨别性通常是形状匹配和物体识别等计算机视觉问题中的两个“冲突”因素。例如，据观察，强变形不变描述符常常遭受类别识别辨别能力低的问题。这个 EAGER 项目探索了一种平衡计算机视觉任务的可变形性和可辨别性的新框架。该框架将一个对象（可以是 2D 形状、点集、图像、3D 体积或表面）统一嵌入到称为方面空间的高维空间中。然后使用嵌入参数来控制变形不敏感的程度。对所提出框架的理论和应用方面进行了研究。基于该框架，该项目旨在开发三个额外的研究目标：通过自适应选择变形能力的鲁棒形状匹配方法，通过处理框架中的关节的鲁棒点集配准方法，以及通过提取嵌入方面空间中的特征来鲁棒图像匹配。这些目标计划在实际应用中进行评估，包括基于轮廓的树叶数据检索、基于计算机的物理治疗中的 3D 标记匹配以及基于图像的疾病筛查。该项目旨在解决处理变形和提高可辨别性这两个主要问题，这涉及计算机视觉内外的许多子领域。跨学科应用预计将对生物多样性研究、生物医学研究等各个领域产生重大贡献。研究成果，包括代码和数据，通过项目网站公开。