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Shape-constrained Gaussian process regression for surface reconstruction and multimodal, non-rigid image registration
Journal of Applied Statistics ( IF 1.5 ) Pub Date : 2021-03-10 , DOI: 10.1080/02664763.2021.1897970
Thomas Deregnaucourt 1 , Chafik Samir 1 , Sebastian Kurtek 2 , Anne-Francoise Yao 3
Affiliation  

We present a new statistical framework for landmark ?>curve-based image registration and surface reconstruction. The proposed method first elastically aligns geometric features (continuous, parameterized curves) to compute local deformations, and then uses a Gaussian random field model to estimate the full deformation vector field as a spatial stochastic process on the entire surface or image domain. The statistical estimation is performed using two different methods: maximum likelihood and Bayesian inference via Markov Chain Monte Carlo sampling. The resulting deformations accurately match corresponding curve regions while also being sufficiently smooth over the entire domain. We present several qualitative and quantitative evaluations of the proposed method on both synthetic and real data. We apply our approach to two different tasks on real data: (1) multimodal medical image registration, and (2) anatomical and pottery surface reconstruction.



中文翻译:

用于表面重建和多模态、非刚性图像配准的形状约束高斯过程回归

我们提出了一个新的地标统计框架?>基于曲线的图像配准和表面重建。所提出的方法首先弹性对齐几何特征(连续、参数化曲线)以计算局部变形,然后使用高斯随机场模型将完整变形矢量场估计为整个表面或图像域上的空间随机过程。使用两种不同的方法执行统计估计:通过马尔可夫链蒙特卡罗采样的最大似然和贝叶斯推断。产生的变形准确地匹配相应的曲线区域,同时在整个域上也足够平滑。我们在合成数据和真实数据上对所提出的方法进行了几种定性和定量评估。

更新日期:2021-03-10
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