MSClique: multiple structure discovery through the maximum weighted clique problem

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Sanromà, Gerard
• dc.contributor.author Penate-Sanchez, Adrian
• dc.contributor.author Alquézar, René
• dc.contributor.author Serratosa, Francesc
• dc.contributor.author Moreno-Noguer, Francesc
• dc.contributor.author Andrade-Cetto, Juan
• dc.contributor.author González Ballester, Miguel Ángel, 1973-
• dc.date.accessioned 2025-01-13T08:42:21Z
• dc.date.available 2025-01-13T08:42:21Z
• dc.date.issued 2016
• dc.description.abstract 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.en
• dc.description.sponsorship This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under Project DPI-2011-27510 and the ERA-Net Chistera project ViSen PCIN-2013-047. This work has also been partially supported by EU H2020, Call H2020-ICT-23-2014-1 (RIA) under Project 644271. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Sanroma G, Penate-Sanchez A, Alquézar R, Serratosa F, Moreno-Noguer F, Andrade-Cetto J, et al. MSClique: multiple structure discovery through the maximum weighted clique problem. PLoS ONE. 2016 Jan 14;11(1):e0145846. DOI: 10.1371/journal.pone.0145846
• dc.identifier.doi http://dx.doi.org/10.1371/journal.pone.0145846
• dc.identifier.issn 1932-6203
• dc.identifier.uri http://hdl.handle.net/10230/69071
• dc.language.iso eng
• dc.publisher Public Library of Science (PLoS)
• dc.relation.ispartof PLoS ONE. 2016 Jan 14;11(1):e0145846
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/644271
• dc.rights © 2016 Sanroma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.other Imatgeria (Tècnica)ca
• dc.subject.other Algorismesca
• dc.subject.other Permutacionsca
• dc.title MSClique: multiple structure discovery through the maximum weighted clique problem
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion