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Multi-Reference Shape Priors for Active Contours

delete2008-09-17
delete47
PRE
AI
A
Alban Foulonneau *
P
Pierre Charbonnier
F
Fabrice Heitz
DOI:10.1007/s11263-008-0163-3delete
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Abstract

Abstract

En 中文
In this paper, we present a new way of constraining the evolution of an active contour with respect to a set of fixed reference shapes. This approach is based on a description of shapes by the Legendre moments computed from their characteristic function. This provides a region-based representation that can handle arbitrary shape topologies. Moreover, exploiting the properties of moments, it is possible to include intrinsic affine invariance in the descriptor, which solves the issue of shape alignment without increasing the number of d.o.f. of the initial problem and allows introducing geometric shape variabilities. Our new shape prior is based on a distance, in terms of descriptors, between the evolving curve and the reference shapes. Minimizing the corresponding shape energy leads to a geometric flow that does not rely on any particular representation of the contour and can be implemented with any contour evolution algorithm. We introduce our prior into a two-class segmentation functional, showing its benefits on segmentation results in presence of severe occlusions and clutter. Examples illustrate the ability of the model to deal with large affine deformation and to take into account a set of reference shapes of different topologies.
Keywords:
Active contours
Segmentation
Region-based approach
Legendre moments
Shape constraint
Shape derivative
Multi-reference model
Geometric invariance
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Journal

International Journal of Computer Vision cover
International Journal of Computer Vision
IF:
9.3
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3.9K
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centre national de la recherche scientifique (cnrs)
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universites de strasbourg etablissements associes
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