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Modèles statistiques pour l'estimation de la matrice fondamentale

Nicolas Noury 1 Frédéric Sur 1 Marie-Odile Berger 1
1 MAGRIT - Visual Augmentation of Complex Environments
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Fundamental matrix estimation between two views is a cornerstone of structure from motion problems. Estimation is usually achieved in a twofold procedure : 1) identify matching points of interest between the two views, and 2) sort out the best matches through a robust filtering. The success of this latter step depends on the accuracy of the former one, and on several thresholds. Setting those thresholds is quite touchy and makes it difficult to automate the whole process. L. Moisan et B. Stival [8] have proposed a statistical model that enables to get rid of these thresholds. We assess over real and synthetic data that this model performs better than existing ones, especially from a robustness, accuracy, and computation time point of view. Besides, following their works, we propose an integrated algorithm that allows simultaneously to match interest points and to estimate the fundamental matrix between two views. We show that this algorithm is robust toward repeated patterns which are difficult to unambiguously match.
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Nicolas Noury, Frédéric Sur, Marie-Odile Berger. Modèles statistiques pour l'estimation de la matrice fondamentale. Congrès francophone des jeunes chercheurs en vision par ordinateur - ORASIS'07, Jun 2007, Obernai, France. pp.8. ⟨inria-00164805⟩

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