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A penalized likelihood approach to image warping

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  • C. A. Glasbey
  • K. V. Mardia

Abstract

A warping is a function that deforms images by mapping between image domains. The choice of function is formulated statistically as maximum penalized likelihood, where the likelihood measures the similarity between images after warping and the penalty is a measure of distortion of a warping. The paper addresses two issues simultaneously, of how to choose the warping function and how to assess the alignment. A new, Fourier–von Mises image model is identified, with phase differences between Fourier‐transformed images having von Mises distributions. Also, new, null set distortion criteria are proposed, with each criterion uniquely minimized by a particular set of polynomial functions. A conjugate gradient algorithm is used to estimate the warping function, which is numerically approximated by a piecewise bilinear function. The method is motivated by, and used to solve, three applied problems: to register a remotely sensed image with a map, to align microscope images obtained by using different optics and to discriminate between species of fish from photographic images.

Suggested Citation

  • C. A. Glasbey & K. V. Mardia, 2001. "A penalized likelihood approach to image warping," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 465-492.
    • Handle: RePEc:bla:jorssb:v:63:y:2001:i:3:p:465-492
    DOI: 10.1111/1467-9868.00295
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    Cited by:

    1. Olivier Collier & Arnak S, Dalalyan, 2013. "Curve registration by Nonparametric goodness-of-fit Testing," Working Papers 2013-33, Center for Research in Economics and Statistics.
    2. Cavan Reilly & Phillip Price & Andrew Gelman & Scott A. Sandgathe, 2004. "Using Image and Curve Registration for Measuring the Goodness of Fit of Spatial and Temporal Predictions," Biometrics, The International Biometric Society, vol. 60(4), pages 954-964, December.
    3. Allassonnière, Stéphanie & Kuhn, Estelle, 2015. "Convergent stochastic Expectation Maximization algorithm with efficient sampling in high dimension. Application to deformable template model estimation," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 4-19.
    4. Gerda Claeskens & Bernard W. Silverman & Leen Slaets, 2010. "A multiresolution approach to time warping achieved by a Bayesian prior–posterior transfer fitting strategy," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(5), pages 673-694, November.

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