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Rigid and articulated point registration with expectation conditional maximization.

Radu Horaud1, Florence Forbes, Manuel Yguel

  • 1INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin, France. radu.horaud@inrialpes.fr

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 28, 2010
PubMed
Summary

This study introduces a new algorithm for matching shapes using probabilistic point registration. The Expectation Conditional Maximization for Point Registration (ECMPR) algorithm improves accuracy and robustness in shape matching.

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Area of Science:

  • Computer Vision
  • Computational Geometry
  • Pattern Recognition

Background:

  • Shape matching is crucial in computer vision and graphics.
  • Probabilistic methods offer robust solutions for point registration.
  • Existing methods often struggle with complex shapes and outliers.

Purpose of the Study:

  • To develop an advanced algorithm for rigid and articulated shape matching.
  • To improve the accuracy and robustness of point registration techniques.
  • To handle unknown correspondences and outliers effectively.

Main Methods:

  • Recasting shape matching as a missing data problem using mixture models.
  • Introducing the Expectation Conditional Maximization for Point Registration (ECMPR) algorithm.
  • Utilizing general covariance matrices and semidefinite positive relaxation for parameter estimation.
  • Incorporating a uniform component for outlier rejection in Gaussian mixture models.

Main Results:

  • The ECMPR algorithm demonstrates improved performance over existing methods.
  • The method effectively handles both rigid and articulated shape registration.
  • Robustness is achieved through effective outlier detection and rejection.
  • Theoretical and experimental comparisons validate the proposed approach.

Conclusions:

  • The ECMPR algorithm offers a significant advancement in probabilistic point registration.
  • The method provides a robust and accurate solution for complex shape matching tasks.
  • This work contributes to the field of computer vision with a novel registration technique.