Efficient and Scalable 4-th order Match Propagation

David Ok 1, 2, * Renaud Marlet 1, 2, 3, 4 Jean-Yves Audibert 1, 2, 5
* Auteur correspondant
1 IMAGINE [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech
5 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : We propose a robust method to match image feature points taking into account geometric consistency. It is a careful adaptation of the match propagation principle to 4th-order geometric constraints (match quadruple consistency). With our method, a set of matches is explained by a network of locally-similar affinities. This approach is useful when simple descriptor-based matching strategies fail, in particular for highly ambiguous data, e.g., with repetitive patterns or where texture is lacking. As it scales easily to hundreds of thousands of matches, it is also useful when denser point distributions are sought, e.g., for high-precision rigid model estimation. Experiments show that our method is competitive (efficient, scalable, accurate, robust) against state-of-the-art methods in deformable object matching, camera calibration and pattern detection.
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Soumis le : jeudi 18 octobre 2012 - 01:48:36
Dernière modification le : mardi 26 février 2019 - 10:14:09

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David Ok, Renaud Marlet, Jean-Yves Audibert. Efficient and Scalable 4-th order Match Propagation. 11th Asian Conference on Computer Vision (ACCV 2012), Nov 2012, Daejeon, South Korea. pp.460-473, ⟨10.1007/978-3-642-37331-2_35⟩. ⟨hal-00743044⟩

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