Distance-Based Shape Statistics

Guillaume Charpiat 1 Pierre Maurel 1, 2 Olivier Faugeras 1 Renaud Keriven 1, 3
1 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique de l'École normale supérieure, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
2 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : This article deals with statistics on sets of shapes. The approach is based on the Hausdorff distance between shapes. The choice of the Hausdorff distance between shapes is itself not fundamental since the same framework could be applied with another distance. We first define a smooth approximation of the Hausdorff distance and build non-supervised warpings between shapes by a gradient descent of the approximation. Local minima can be avoided by changing the scalar product in the tangent space of the shape being warped.When non-supervised warping fails, we present a way to guide the evolution with a small number of landmarks. Thanks to the warping fields, we can define the mean of a set of shapes and express statistics on them. Finally, we come back to the initial distance between shapes and use it to represent a set of shapes by a graph, which with the technic of graph Laplacian leads to a way of projecting shapes onto a low dimensional space.
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Conference papers
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https://hal.inria.fr/inria-00608087
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Submitted on : Tuesday, July 12, 2011 - 10:49:53 AM
Last modification on : Monday, March 4, 2019 - 2:07:55 PM
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Guillaume Charpiat, Pierre Maurel, Olivier Faugeras, Renaud Keriven. Distance-Based Shape Statistics. 31st International Conference on Acoustics, Speech, and Signal Processing, May 2006, Toulouse, France. pp.V925-V928, ⟨10.1109/ICASSP.2006.1661428⟩. ⟨inria-00608087⟩

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