Variational methods for multimodal image matching
Variational methods for multimodal image matching
复制标题
DOI：
10.1023/a:1020830525823
复制
发表时间：
2002-12-01
影响因子：
19.5
通讯作者：
Faugeras, O
中科院分区：
文献类型：
--
作者：
Hermosillo, G;Chefd'Hotel, C;Faugeras, O
不同模态的匹配图像可以通过在给定类别的几何变换内最大化合适的统计相似性度量来实现。在这种情况下，处理复杂的，非刚性变形是特别困难的，并在过去几年中引起了人们的广泛关注。本文的主旨是，许多现有的非刚性单模配准方法，使用简单的标准来比较强度（如。G. SSD）可以扩展到多模态情况，其中需要更复杂的强度相似性度量。为此，我们进行了一个正式的计算的变分梯度的层次结构的统计相似性措施，并使用结果推广最近提出的和非常有效的光流算法（L。Alvarez，J. Weickert和J. Sanchez，2000，Technical Report，和IJCV 39（1）：41-56）。我们的方法很容易扩展到本地计算的相似性措施的情况下，从而提供了灵活性，以科普空间非平稳性的方式在两个图像的强度相关。所得到的方程的适定性证明在补充工作（O.D. Faugeras和G. Hermosillo，2001，Technical Report 4235，INRIA），使用功能分析中的成熟技术。我们简要地描述了我们的数值实现这些方程，并显示结果的真实的和合成数据。
Matching images of different modalities can be achieved by the maximization of suitable statistical similarity measures within a given class of geometric transformations. Handling complex, nonrigid deformations in this context turns out to be particularly difficult and has attracted much attention in the last few years. The thrust of this paper is that many of the existing methods for nonrigid monomodal registration that use simple criteria for comparing the intensities (e. g. SSD) can be extended to the multimodal case where more complex intensity similarity measures are necessary. To this end, we perform a formal computation of the variational gradient of a hierarchy of statistical similarity measures, and use the results to generalize a recently proposed and very effective optical flow algorithm (L. Alvarez, J. Weickert, and J. Sanchez, 2000, Technical Report, and IJCV 39(1):41-56) to the case of multimodal image registration. Our method readily extends to the case of locally computed similarity measures, thus providing the flexibility to cope with spatial non-stationarities in the way the intensities in the two images are related. The well posedness of the resulting equations is proved in a complementary work (O.D. Faugeras and G. Hermosillo, 2001, Technical Report 4235, INRIA) using well established techniques in functional analysis. We briefly describe our numerical implementation of these equations and show results on real and synthetic data.