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Elastically adaptive deformable models

  • Dimitri Metaxas
  • Ioannis A. Kakadiaris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1065)

Abstract

We present a novel technique for the automatic adaptation of a deformable model's elastic parameters within a Kalman filter frame-work for shape estimation applications. The novelty of the technique is that the model's elastic parameters are not constant, but time varying. The model for the elastic parameter variation depends on the local error of fit and the rate of change of the error of fit. By augmenting the state equations of an extended Kalman filter to incorporate these additional variables and take into account the noise in the data, we are able to significantly improve the quality of the shape estimation. Therefore, the model's elastic parameters are initialized always to the same value and they subsequently modified depending on the data and the noise distribution. In addition, we demonstrate how this technique can be parallelized in order to increase its efficiency. We present several experiments to demonstrate the effectiveness of our method.

Keywords

Kalman Filter Extended Kalman Filter Elastic Parameter Deformable Model Active Contour Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Dimitri Metaxas
    • 1
  • Ioannis A. Kakadiaris
    • 1
  1. 1.Department of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphia

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