A new point matching algorithm for non-rigid registration

نویسندگان

  • Haili Chui
  • Anand Rangarajan
چکیده

Feature-based methods for non-rigid registration frequently encounter the correspondence problem. Regardless of whether points, lines, curves or surface parameterizations are used, feature-based non-rigid matching requires us to automatically solve for correspondences between two sets of features. In addition, there could be many features in either set that have no counterparts in the other. This outlier rejection problem further complicates an already difficult correspondence problem. We formulate feature-based non-rigid registration as a nonrigid point matching problem. After a careful review of the problem and an in-depth examination of two types of methods previously designed for rigid robust point matching (RPM), we propose a new general framework for non-rigid point matching. We consider it a general framework because it does not depend on any particular form of spatial mapping. We have also developed an algorithm—the TPS–RPM algorithm—with the thin-plate spline (TPS) as the parameterization of the non-rigid spatial mapping and the softassign for the correspondence. The performance of the TPS–RPM algorithm is demonstrated and validated in a series of carefully designed synthetic experiments. In each of these experiments, an empirical comparison with the popular iterated closest point (ICP) algorithm is also provided. Finally, we apply the algorithm to the problem of non-rigid registration of cortical anatomical structures which is required in brain mapping. While these results are somewhat preliminary, they clearly demonstrate the applicability of our approach to real world tasks involving feature-based nonrigid registration. 2003 Published by Elsevier Science (USA). Computer Vision and Image Understanding 89 (2003) 114–141 www.elsevier.com/locate/cviu Corresponding author. E-mail addresses: [email protected] (H. Chui), [email protected] (A. Rangarajan). 1077-3142/03/$ see front matter 2003 Published by Elsevier Science (USA). doi:10.1016/S1077-3142(03)00009-2

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عنوان ژورنال:
  • Computer Vision and Image Understanding

دوره 89  شماره 

صفحات  -

تاریخ انتشار 2003