Nonlinear shape registration without correspondences

Csaba Domokos, Jozsef Nemeth, Zoltan Kato

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

In this paper, we propose a novel framework to estimate the parameters of a diffeomorphism that aligns a known shape and its distorted observation. Classical registration methods first establish correspondences between the shapes and then compute the transformation parameters from these landmarks. Herein, we trace back the problem to the solution of a system of nonlinear equations which directly gives the parameters of the aligning transformation. The proposed method provides a generic framework to recover any diffeomorphic deformation without established correspondences. It is easy to implement, not sensitive to the strength of the deformation, and robust against segmentation errors. The method has been applied to several commonly used transformation models. The performance of the proposed framework has been demonstrated on large synthetic data sets as well as in the context of various applications.

Original languageEnglish
Article number6042875
Pages (from-to)943-958
Number of pages16
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume34
Issue number5
DOIs
Publication statusPublished - Apr 4 2012

    Fingerprint

Keywords

  • Image registration
  • diffeomorphism
  • nonlinear transformation
  • planar homography
  • shape matching.
  • thin plate spline

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

Cite this