University of California, Davis, Davis.
IEEE Trans Pattern Anal Mach Intell. 2014 Mar;36(3):466-78. doi: 10.1109/TPAMI.2013.139.
A new algorithm is presented that provides a constructive way to conformally warp a triangular mesh of genus zero to a destination surface with minimal metric deformation, as well as a means to compute automatically a measure of the geometric difference between two surfaces of genus zero. The algorithm takes as input a pair of surfaces that are topological 2-spheres, each surface given by a distinct triangulation. The algorithm then constructs a map $(f)$ between the two surfaces. First, each of the two triangular meshes is mapped to the unit sphere using a discrete conformal mapping algorithm. The two mappings are then composed with a Möbius transformation to generate the function $(f)$. The Möbius transformation is chosen by minimizing an energy that measures the distance of $(f)$ from an isometry. We illustrate our approach using several "real life" data sets. We show first that the algorithm allows for accurate, automatic, and landmark-free nonrigid registration of brain surfaces. We then validate our approach by comparing shapes of proteins. We provide numerical experiments to demonstrate that the distances computed with our algorithm between low-resolution, surface-based representations of proteins are highly correlated with the corresponding distances computed between high-resolution, atomistic models for the same proteins.
提出了一种新算法,该算法提供了一种将零亏格的三角网格贴合到具有最小度量变形的目标曲面的构造方法,以及一种自动计算两个零亏格曲面之间几何差异的度量的方法。该算法的输入是一对拓扑为 2-球体的曲面,每个曲面由不同的三角剖分给出。然后,该算法在两个曲面之间构建一个映射$(f)$。首先,使用离散共形映射算法将两个三角网格映射到单位球面上。然后,将两个映射与 Möbius 变换组合,生成函数$(f)$。Möbius 变换是通过最小化度量$(f)$与等距映射的距离的能量来选择的。我们使用几个“现实生活”数据集来说明我们的方法。我们首先表明,该算法允许对大脑表面进行准确、自动和无特征的非刚性配准。然后,我们通过比较蛋白质的形状来验证我们的方法。我们提供数值实验来证明,我们算法计算的低分辨率、基于曲面的蛋白质表示之间的距离与同一蛋白质的高分辨率、原子模型之间的相应距离高度相关。