Statistical models for deformable templates in image and shape analysis

Stéphanie Allassonnière[1]; Jérémie Bigot[2]; Joan Alexis Glaunès[3]; Florian Maire[4]; Frédéric J.P. Richard[5]

  • [1] CMAP Ecole Polytechnique Route de Saclay 91128 Palaiseau FRANCE
  • [2] Institut de Mathématiques de Toulouse, CNRS UMR 5219 Université de Toulouse 118 route de Narbonne 31062 Toulouse Cedex 9 FRANCE
  • [3] MAP5 Université Paris Descartes, Sorbonne Paris Cité 45 rue des Saints-Pères 75270 Paris Cedex 06 FRANCE
  • [4] ONERA - The French Aerospace Lab F-91761 Palaiseau FRANCE
  • [5] LATP CNRS UMR 7353 Aix Marseille Université Centre de mathématiques et d’informatique 39 rue Frédéric Joliot 13453 Marseille Cedex FRANCE

Annales mathématiques Blaise Pascal (2013)

  • Volume: 20, Issue: 1, page 1-35
  • ISSN: 1259-1734

Abstract

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High dimensional data are more and more frequent in many application fields. It becomes particularly important to be able to extract meaningful features from these data sets. Deformable template model is a popular way to achieve this. This paper is a review on the statistical aspects of this model as well as its generalizations. We describe the different mathematical frameworks to handle different data types as well as the deformations. We recall the theoretical convergence properties of the estimators and the numerical algorithm to achieve them. We end with some published examples.

How to cite

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Allassonnière, Stéphanie, et al. "Statistical models for deformable templates in image and shape analysis." Annales mathématiques Blaise Pascal 20.1 (2013): 1-35. <http://eudml.org/doc/275647>.

@article{Allassonnière2013,
abstract = {High dimensional data are more and more frequent in many application fields. It becomes particularly important to be able to extract meaningful features from these data sets. Deformable template model is a popular way to achieve this. This paper is a review on the statistical aspects of this model as well as its generalizations. We describe the different mathematical frameworks to handle different data types as well as the deformations. We recall the theoretical convergence properties of the estimators and the numerical algorithm to achieve them. We end with some published examples.},
affiliation = {CMAP Ecole Polytechnique Route de Saclay 91128 Palaiseau FRANCE; Institut de Mathématiques de Toulouse, CNRS UMR 5219 Université de Toulouse 118 route de Narbonne 31062 Toulouse Cedex 9 FRANCE; MAP5 Université Paris Descartes, Sorbonne Paris Cité 45 rue des Saints-Pères 75270 Paris Cedex 06 FRANCE; ONERA - The French Aerospace Lab F-91761 Palaiseau FRANCE; LATP CNRS UMR 7353 Aix Marseille Université Centre de mathématiques et d’informatique 39 rue Frédéric Joliot 13453 Marseille Cedex FRANCE},
author = {Allassonnière, Stéphanie, Bigot, Jérémie, Glaunès, Joan Alexis, Maire, Florian, Richard, Frédéric J.P.},
journal = {Annales mathématiques Blaise Pascal},
keywords = {Review paper; Deformable template model; statistical analysis; review paper; deformable template model},
language = {eng},
month = {1},
number = {1},
pages = {1-35},
publisher = {Annales mathématiques Blaise Pascal},
title = {Statistical models for deformable templates in image and shape analysis},
url = {http://eudml.org/doc/275647},
volume = {20},
year = {2013},
}

TY - JOUR
AU - Allassonnière, Stéphanie
AU - Bigot, Jérémie
AU - Glaunès, Joan Alexis
AU - Maire, Florian
AU - Richard, Frédéric J.P.
TI - Statistical models for deformable templates in image and shape analysis
JO - Annales mathématiques Blaise Pascal
DA - 2013/1//
PB - Annales mathématiques Blaise Pascal
VL - 20
IS - 1
SP - 1
EP - 35
AB - High dimensional data are more and more frequent in many application fields. It becomes particularly important to be able to extract meaningful features from these data sets. Deformable template model is a popular way to achieve this. This paper is a review on the statistical aspects of this model as well as its generalizations. We describe the different mathematical frameworks to handle different data types as well as the deformations. We recall the theoretical convergence properties of the estimators and the numerical algorithm to achieve them. We end with some published examples.
LA - eng
KW - Review paper; Deformable template model; statistical analysis; review paper; deformable template model
UR - http://eudml.org/doc/275647
ER -

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