Efficient and Scalable 4-th order Match Propagation

David Ok (1, 2) , Renaud Marlet (1, 2, 3, 4) , Jean-Yves Audibert (1, 2, 5)

1 imagine [Marne-la-Vallée]
2 LIGM - Laboratoire d'Informatique Gaspard-Monge
3 UPEM - Université Paris-Est Marne-la-Vallée
4 ENPC - École des Ponts ParisTech
5 SIERRA - Statistical Machine Learning and Parsimony

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.

Domaines: Informatique [cs]; Vision par ordinateur et reconnaissance de formes [cs.CV]

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⟩