In this paper, a new statistical method to model patterns emerging in complex systems is proposed. A framework for shape analysis of 2− dimensional landmark data is introduced, in which each landmark is represented by a bivariate Gaussian distribution. From Information Geometry we know that Fisher-Rao metric endows the statistical manifold of parameters of a family of probability distributions with a Riemannian metric. Thus this approach allows to reconstruct the intermediate steps in the evolution between observed shapes by computing the geodesic, with respect to the Fisher-Rao metric, between the corresponding distributions. Furthermore, the geodesic path can be used for shape predictions. As application, we study the evolution of the rat skull shape. A future application in Ophthalmology is introduced

Methods of Information Geometry to model complex shapes

De Sanctis, A.;Gattone, S. A.
2016-01-01

Abstract

In this paper, a new statistical method to model patterns emerging in complex systems is proposed. A framework for shape analysis of 2− dimensional landmark data is introduced, in which each landmark is represented by a bivariate Gaussian distribution. From Information Geometry we know that Fisher-Rao metric endows the statistical manifold of parameters of a family of probability distributions with a Riemannian metric. Thus this approach allows to reconstruct the intermediate steps in the evolution between observed shapes by computing the geodesic, with respect to the Fisher-Rao metric, between the corresponding distributions. Furthermore, the geodesic path can be used for shape predictions. As application, we study the evolution of the rat skull shape. A future application in Ophthalmology is introduced
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/691507
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