Geodesic regression for image time-series.
Geodesic regression for image time-series.
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DOI：
10.1007/978-3-642-23629-7_80
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发表时间：
2011
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影响因子：
--
通讯作者：
Vialard, Francois-Xavier
中科院分区：
文献类型：
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作者：
Niethammer, Marc;Huang, Yang;Vialard, Francois-Xavier
注册的图像时间序列迄今已完成（i）通过连接注册之间的图像对，（ii）通过解决一个联合估计问题，导致图像对之间的分段测地线路径，（iii）通过基于内核的本地平均或（iv）通过增加额外的时间不规则性处罚的联合估计。在这里，我们提出了一个生成模型扩展最小二乘线性回归的图像空间，通过使用二阶动态配方的图像配准。与以前的方法不同，该制剂允许通过其初始值的完整时空轨迹的近似的紧凑表示。该方法还开辟了可能性，设计基于图像的近似算法。由此产生的优化问题使用伴随方法来解决。
Registration of image-time series has so far been accomplished (i) by concatenating registrations between image pairs, (ii) by solving a joint estimation problem resulting in piecewise geodesic paths between image pairs, (iii) by kernel based local averaging or (iv) by augmenting the joint estimation with additional temporal irregularity penalties. Here, we propose a generative model extending least squares linear regression to the space of images by using a second-order dynamic formulation for image registration. Unlike previous approaches, the formulation allows for a compact representation of an approximation to the full spatio-temporal trajectory through its initial values. The method also opens up possibilities to design image-based approximation algorithms. The resulting optimization problem is solved using an adjoint method.