[en] A statistical model for shapes in $\mathbb{R}^2$ or $\mathbb{R}^3$ is proposed. Shape modelling is a difficult problem mainly due to the non-linear nature of its space. Our approach considers curves as shape contours, and models their deformations with respect to a deformable template shape. Contours are uniformly sampled into a discrete sequence of points. Hence, the deformation of a shape is formulated as an action of transformation matrices on each of these points. A parametrized stochastic model based on Markov process is proposed to model shape variability in the deformation space. The model's parameters are estimated from a labeled training dataset. Moreover, a similarity metric based on the Mahalanobis distance is proposed. Subsequently, the model has been successfully tested for shape recognition, synthesis, and retrieval.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
DEMISSE, Girum ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
AOUADA, Djamila ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
OTTERSTEN, Björn ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Template-Based Statistical Shape Modelling on Deformation Space
Date de publication/diffusion :
2015
Nom de la manifestation :
22nd IEEE International Conference on Image Processing
Organisateur de la manifestation :
IEEE Signal Processing Society in conjunction with Institute of Elecetrical and Electronics Engineers
Lieu de la manifestation :
Quebec city, Canada
Date de la manifestation :
from 27-09-2015 to 30-09-2015
Manifestation à portée :
International
Titre de l'ouvrage principal :
22nd IEEE International Conference on Image Processing
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