Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/119155
Título: MSClique: Multiple Structure Discovery through the Maximum Weighted Clique Problem
Autores/as: Sanroma, Gerard
Peñate Sánchez, Adrián 
Alquezar, René
Serratosa, Francesc
Moreno-Noguer, Francesc
Andrade-Cetto, Juan
González Ballester, Miguel Ángel
Coordinadores/as, Directores/as o Editores/as: Yap, Pew-Thian
Clasificación UNESCO: 1203 Ciencia de los ordenadores
Fecha de publicación: 2016
Publicación seriada: PLoS ONE 
Resumen: We present a novel approach for feature correspondence and multiple structure discovery in computer vision. In contrast to existing methods, we exploit the fact that point-sets on the same structure usually lie close to each other, thus forming clusters in the image. Given a pair of input images, we initially extract points of interest and extract hierarchical representations by agglomerative clustering. We use the maximum weighted clique problem to find the set of corresponding clusters with maximum number of inliers representing the multiple structures at the correct scales. Our method is parameter-free and only needs two sets of points along with their tentative correspondences, thus being extremely easy to use. We demonstrate the effectiveness of our method in multiple-structure fitting experiments in both publicly available and in-house datasets. As shown in the experiments, our approach finds a higher number of structures containing fewer outliers compared to state-of-the-art methods.
URI: http://hdl.handle.net/10553/119155
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0145846
Fuente: PLoS ONE [ISSN 1932-6203], v. 11 (1), art. e0145846 (2016)
Colección:Artículos
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