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

在形状匹配和目标识别等计算机视觉问题中，变形性和可分辨性往往是两个“冲突”的因素。例如，已经观察到，强变形不变描述符通常遭受用于类别识别的低辨别能力。这个EAGER项目探索了一个新的框架，用于平衡计算机视觉任务的变形性和可辨别性。该框架将二维形状、点集、图像、三维体或表面等对象统一嵌入到一个称为方面空间的高维空间中。然后，嵌入参数用于控制变形不敏感的程度。所提出的框架的理论和应用方面的调查。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连接的鲁棒点集配准方法，以及通过在嵌入的方面空间中提取特征的鲁棒图像匹配。这些目标计划在真实的应用中进行评估，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。该项目旨在弥合两个主要问题，处理变形和提高可分辨性，这涉及到计算机视觉内外的许多子领域。跨学科的应用，预计将产生重大贡献的各个领域，包括生物多样性研究，生物医学研究等的研究成果，包括代码和数据，通过项目网站公开。

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.