Relational Shape Matching for Registration and Recognition

用于注册和识别的关系形状匹配

• 批准号: 0307712
• 负责人: Anand Rangarajan
• 金额: $ 22.17万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0307712&HistoricalAwards=false
• 关键词: Relational; Shape; Matching; Registration; Recognition

Proposal #: 0307712Title: Relational Shape Matching for Registration and RecognitionPI: Rangarajan, AnandUniversity of FloridaShape matching is an important subfield of computer vision and has a host of applications in object recognition, non-rigid registration in medical imaging, and indexing in image databases. In medical imaging for example, a fundamental advance in shape matching will have important ramifications for the automated segmentation and classification of anatomical structures. In object recognition, advances in shape matching will be enormously useful in constructing new distance measures that can be used for indexing and retrieval.Shape matching involves establishing correspondences between homologous structures in different objects. While the correspondence problem can be avoided if intensity-based approaches are used, these methods often rely on the brightness constancy assumption that is often invalid. Feature-based approaches to shape matching have to solve the correspondence problem in situations where there are significant global and local shape differences between the objects being compared. In non-rigid registration, a shape matching approach is required to objects into register. In this work, a relational shape matching approach for simultaneously solving for correspondence and non-rigid deformations is proposed. In contrast to most previous work, the shape matching objective function is set up as a pairwise correspondence and deformation problem. The template consists of a point-set and a known topology that can easily accommodate curves and surfaces. In sharp contrast to previous graph matching approaches, the data is represented as an unstructured point-set. While the resulting optimization problem may appear formidable, efficient algorithms can be designed based on recent and fundamental advances in Bayesian networks. The Bayesian network approach recasts the pairwise correspondence as a joint probability and this results in an alternating algorithm that iteratively updates joint probabilities and deformations. Assuming that efficient algorithms can be designed based on this approach, both non-rigid registration and object recognition applications can be tackled using the same framework and algorithm. Validation and evaluation of these algorithms will be done using medical imaging datasets. Comparisons will also be undertaken against our own previous NSF-funded non-rigid point matching (TPS-RPM) algorithms.Despite mostly being confined to computer vision, shape matching has the potential to reach a much broader audience with computer graphics and theoretical physics being two concrete examples. In computer graphics, there is burgeoning interest in matching point clouds and more structured representations such as surfaces. While theoretical physics seems like an unlikely candidate at first glance, there is a deep connection between the shape matching objective functions proposed here and by many other vision researchers and general relativity; evidence of this connection can be seen in the recent work of Julian Barbour,The End of Time, Oxford Univ. Press, 2000 (Chapter 11). These connections are just beginning to be noticed and come as a surprise to both vision and theoretical physics researchers. As shape matching in computer vision continues to make incremental (and hopefully inexorable) progress in both frameworks and efficient algorithms, it is hoped that these deep connections will result in unexpected and productive cross-fertilizations.

提案＃：0307712TITLE：注册和认可的关系形状匹配：Rangarajan，Floridashape匹配的Ananduniversity是计算机视觉的重要子场，并且在对象识别，非rigid注册中具有医学成像中的非riGID注册以及在Image Databases中进行索引。例如，在医学成像中，形状匹配的基本进步将对解剖结构的自动分割和分类具有重要的后果。在对象识别中，形状匹配的进步将在构建可用于索引和检索的新距离度量方面非常有用。形状匹配涉及在不同对象中的同源结构之间建立对应关系。如果使用基于强度的方法，则可以避免对应问题，但这些方法通常依赖于通常无效的亮度恒定假设。基于特征的形状匹配方法必须在比较对象之间存在明显的全局和局部形状差异的情况下解决对应问题。在非刚性注册中，将形状匹配的方法才能对象进入寄存器。在这项工作中，提出了同时求解对应关系和非刚性变形的关系形状匹配方法。与大多数先前的工作相反，形状匹配的目标函数被设置为成对的对应关系和变形问题。该模板由一个点集和已知拓扑组成，该拓扑可以容易适应曲线和表面。与以前的图形匹配方法形成鲜明对比的是，数据表示为非结构化点集。虽然所得的优化问题可能看起来很强大，但可以根据贝叶斯网络的最新和基本进步设计有效的算法。贝叶斯网络方法将成对的对应关系作为关节概率重铸，这导致了交替的算法，该算法会迭代地更新关节概率和变形。假设可以根据这种方法设计有效的算法，则可以使用相同的框架和算法来解决非刚性注册和对象识别应用程序。这些算法的验证和评估将使用医学成像数据集进行。比较还将与我们自己以前的NSF资助的非刚性点匹配（TPS-RPM）算法进行比较。DDITE主要限于计算机视觉，Shape匹配有可能通过计算机图形图和理论物理学是两个具体示例来吸引更广泛的受众。在计算机图形学中，人们对匹配点云和更结构化表示（例如表面）引起了人们的兴趣。虽然乍看之下理论物理学似乎是不太可能的候选者，但这里提出的形状匹配的目标功能与许多其他视觉研究人员与一般相对论之间存在着深厚的联系。牛津大学的朱利安·巴伯（Julian Barbour）最近的工作，朱利安·巴伯（Julian Barbour）的最新工作中可以看到这种联系的证据。出版社，2000年（第11章）。这些联系才刚刚开始引起注意，这对愿景和理论物理研究人员来说都是一个惊喜。随着计算机视觉中的形状匹配继续在框架和有效算法中取得增量（并希望无法令人难以置信的）进展，希望这些深层连接将导致意外且富有成效的交叉施用。