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The softassign Procrustes matching algorithm

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1230)

Abstract

The problem of matching shapes parameterized as a set of points is frequently encountered in medical imaging tasks. When the point-sets are derived from landmarks, there is usually no problem of determining the correspondences or homologies between the two sets of landmarks. However, when the point sets are automatically derived from images, the difficult problem of establishing correspondence and rejecting non-homologies as outliers remains. The Procrustes method is a well-known method of shape comparison and can always be pressed into service when homologies between point-sets are known in advance. This paper presents a powerful extension of the Procrustes method to pointsets of differing point counts with correspondences unknown. The result is the softassign Procrustes matching algorithm which iteratively establishes correspondence, rejects non-homologies as outliers, determines the Procrustes rescaling and the spatial mapping between the point-sets.

Keywords

  • Point Count
  • Spatial Mapping
  • Permutation Matrix
  • Lagrange Parameter
  • Procrustes Distance

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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© 1997 Springer-Verlag Berlin Heidelberg

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Rangarajan, A., Chui, H., Bookstein, F.L. (1997). The softassign Procrustes matching algorithm. In: Duncan, J., Gindi, G. (eds) Information Processing in Medical Imaging. IPMI 1997. Lecture Notes in Computer Science, vol 1230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63046-5_3

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  • DOI: https://doi.org/10.1007/3-540-63046-5_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63046-3

  • Online ISBN: 978-3-540-69070-2

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