Fusion de données spatiales (3D) et temporelles (2D+t) pour la reconstruction de scènes 3D+t : deux approches

Abstract : The main focus of this paper is 3D+t shape recovery from 3D spatial data framing 2D+t temporal sequences. Merging these two kind of data yields a temporal sequence describing 3D dynamic of structures of interest by reconstructing the original volume from each 2D frame. 2D to 3D inverse reconstruction is a complex and ill posed problem due to the great deal of distortion and in-depth information loss generated by the projective view in each 2D frame. To handle this critical lack of information, we model relevantly the 3D-to-2D transformation that reduces each 3D image into a 2D one and we match each frame with the 3D image known at a previous time. Assuming that structures evolve scarcely between a couple successive moments, these images are matched by a displacement vector field measuring voxel position variation meanwhile. We embed the luminosty conservation constraint and we propose two approaches to estimate voxel movement over time. We reconstruct gradually the 3D+t sequence by motion compensation. Some applications observing in different modalities 2D+t and 3D data can benefit from this study such as microscopical cell biology or medical imaging.
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Wafa Rekik, Dominique Béréziat, Séverine Dubuisson. Fusion de données spatiales (3D) et temporelles (2D+t) pour la reconstruction de scènes 3D+t : deux approches. [Rapport de recherche] 2008. ⟨inria-00583277⟩

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