Publication
MSClique: Multiple structure discovery through the maximum weighted clique problem
Journal Article (2016)
Journal
PLOS One
Pages
e0145846
Volume
11
Number
1
Doc link
http://dx.doi.org/10.1371/journal.pone.0145846
File
Authors
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Sanromà Güell, Gerard
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Peñate Sánchez, Adrián
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Alquézar Mancho, Renato
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Serratosa Casanelles, Francesc
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Moreno Noguer, Francesc
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Andrade Cetto, Juan
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González Ballester, Miguel Angel
Projects associated
PAU+: Perception and Action in Robotics Problems with Large State Spaces
ViSen: Visual Sense, Tagging visual data with semantic descriptions
Robot-Int-Coop: Robot-Human Interaction, Cooperation and Learning in Urban Areas
AEROARMS: AErial RObotics System integrating multiple ARMS and advanced manipulation capabilities for inspection and maintenance
RobInstruct: Instructing robots using natural communication skills
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.
Categories
computer vision.
Scientific reference
G. Sanromà, A. Penate-Sanchez, R. Alquézar Mancho, F. Serratosa, F. Moreno-Noguer, J. Andrade-Cetto and M.A. González. MSClique: Multiple structure discovery through the maximum weighted clique problem. PLOS One, 11(1): e0145846, 2016.
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