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Geodesic matching of triangulated surfaces

机译:三角面的测地匹配

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Recognition of images and shapes has long been the central theme of computer vision. Its importance is increasing rapidly in the field of computer graphics and multimedia communication because it is difficult to process information efficiently without its recognition. In this paper, we propose a new approach for object matching based on a global geodesic measure. The key idea behind our methodology is to represent an object by a probabilistic shape descriptor that measures the global geodesic distance between two arbitrary points on the surface of an object. In contrast to the Euclidean distance which is more suitable for linear spaces, the geodesic distance has the advantage to be able to capture the intrinsic geometric structure of the data. The matching task therefore becomes a one-dimensional comparison problem between probability distributions which is clearly much simpler than comparing three-dimensional structures. Object matching can then be carried out by an information-theoretic dissimilarity measure calculations between geodesic shape distributions, and is additionally computationally efficient and inexpensive.
机译:长期以来,图像和形状的识别一直是计算机视觉的中心主题。它的重要性在计算机图形学和多媒体通信领域中迅速增长,因为难以有效地处理信息而不加以识别。在本文中,我们提出了一种基于全局测地线度量的对象匹配新方法。我们方法论背后的关键思想是用概率形状描述子表示一个对象,该形状描述子测量对象表面上两个任意点之间的全局测地距离。与更适合于线性空间的欧几里得距离相反,测地距离具有能够捕获数据的固有几何结构的优势。因此,匹配任务成为概率分布之间的一维比较问题,这显然比比较三维结构要简单得多。然后可以通过测地线形状分布之间的信息理论相异性度量计算来执行对象匹配,并且该对象匹配还具有计算效率高且成本低廉的特点。

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