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Shape-space smoothing splines for planar landmark data

Author

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  • Alfred Kume
  • Ian L. Dryden
  • Huiling Le

Abstract

A method is developed for fitting smooth curves through a series of shapes of landmarks in two dimensions using unrolling and unwrapping procedures in Riemannian manifolds. An explicit method of calculation is given which is analogous to that of Jupp & Kent (1987) for spherical data. The resulting splines are called shape-space smoothing splines. The method resembles that of fitting smoothing splines in real spaces in that, if the smoothing parameter is zero, the resulting curve interpolates the data points, and if it is infinitely large the curve is a geodesic line. The fitted path to the data is defined such that its unrolled version at the tangent space of the starting point is a cubic spline fitted to the unwrapped data with respect to that path. Computation of the fitted path consists of an iterative procedure which converges quickly, and the resulting path is given in a discretised form in terms of a piecewise geodesic path. The procedure is applied to the analysis of some human movement data, and a test for the appropriateness of a mean geodesic curve is given. Copyright 2007, Oxford University Press.

Suggested Citation

  • Alfred Kume & Ian L. Dryden & Huiling Le, 2007. "Shape-space smoothing splines for planar landmark data," Biometrika, Biometrika Trust, vol. 94(3), pages 513-528.
    • Handle: RePEc:oup:biomet:v:94:y:2007:i:3:p:513-528
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    File URL: http://hdl.handle.net/10.1093/biomet/asm047
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    Citations

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    Cited by:

    1. Kwang‐Rae Kim & Ian L. Dryden & Huiling Le & Katie E. Severn, 2021. "Smoothing splines on Riemannian manifolds, with applications to 3D shape space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(1), pages 108-132, February.
    2. Ian L. Dryden & Kwang-Rae Kim & Huiling Le, 2019. "Bayesian Linear Size-and-Shape Regression with Applications to Face Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 83-103, February.
    3. Victor M. Panaretos & Tung Pham & Zhigang Yao, 2014. "Principal Flows," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 424-436, March.
    4. M. Moghimbeygi & M. Golalizadeh, 2017. "Longitudinal shape analysis by using the spherical coordinates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(7), pages 1282-1295, May.
    5. Stephan F. Huckemann, 2021. "Comments on: Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 71-75, March.
    6. Meisam Moghimbeygi & Mousa Golalizadeh, 2019. "A longitudinal model for shapes through triangulation," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(1), pages 99-121, March.
    7. Chiara Brombin & Luigi Salmaso & Lara Fontanella & Luigi Ippoliti, 2015. "Nonparametric combination-based tests in dynamic shape analysis," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(4), pages 460-484, December.

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