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A Riemannian Framework for Intrinsic Comparison of Closed Genus-Zero Shapes

机译:封闭的属零形状固有比较的黎曼框架

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We present a framework for intrinsic comparison of surface metric structures and curvatures. This work parallels the work of Kurtek et al. on parameterization-invariant comparison of genus zero shapes. Here, instead of comparing the embedding of spherically parameterized surfaces in space, we focus on the first fundamental form. To ensure that the distance on spherical metric tensor fields is invariant to parameterization, we apply the conjugation-invariant metric arising from the L~2 norm on symmetric positive definite matrices. As a reparameterization changes the metric tensor by a congruent Jacobian transform, this metric perfecdy suits our purpose. The result is an intrinsic comparison of shape metric structure that does not depend on the specifics of a spherical mapping. Further, when restricted to tensors of fixed volume form, the manifold of metric tensor fields and its quotient of the group of unitary diffeomorphisms becomes a proper metric manifold that is geodesically complete. Exploiting this fact, and augmenting the metric with analogous metrics on curvatures, we derive a complete Riemannian framework for shape comparison and reconstruction. A by-product of our framework is a near-isometric and curvature-preserving mapping between surfaces. The correspondence is optimized using the fast spherical fluid algorithm. We validate our framework using several subcortical boundary surface models from the ADNI dataset.
机译:我们为表面度量结构和曲率的内在比较提供了一个框架。这项工作与Kurtek等人的工作相似。类零形状的参数化不变比较。在这里,我们没有比较球形参数化曲面在空间中的嵌入,而是关注第一种基本形式。为了确保球度量张量场上的距离对于参数化是不变的,我们对对称正定矩阵应用从L〜2范数产生的共轭不变度量。当重新参数化通过一致的雅可比变换改变度量张量时,该度量性能就适合我们的目的。结果是形状度量结构的内在比较,该比较不依赖于球形映射的细节。此外,当限制为固定体积形式的张量时,度量张量场的流形及其its微分同构群的商将变成一个大地上完整的合适度量流形。利用这一事实,并用类似的曲率度量来扩充度量,我们得出了一个完整的黎曼框架,用于形状比较和重构。我们框架的副产品是曲面之间的近似等距且保持曲率的映射。使用快速球形流体算法优化了对应关系。我们使用来自ADNI数据集中的几个皮质下边界表面模型验证了我们的框架。

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