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dc.contributor.authorSolé-Ribalta, Albert-
dc.contributor.authorSanroma, Gerard-
dc.contributor.authorSerratosa, Francesc-
dc.contributor.authorAlquézar Mancho, Renato-
dc.date.accessioned2014-06-03T07:55:51Z-
dc.date.available2014-06-03T07:55:51Z-
dc.date.issued2012-
dc.identifier.citationProceedings of International Conference on Computer Vision Theory and Applications: 269-278 (2012)-
dc.identifier.urihttp://hdl.handle.net/10261/97606-
dc.descriptionPresentado al VISAPP 2012 celebrado en Roma del 24 al 26 de febrero.-
dc.description.abstractFinding sparse correspondences between two images is a usual process needed for several higher-level computer vision tasks. For instance, in robot positioning, it is frequent to make use of images that the robot captures from their cameras to guide the localisation or reduce the intrinsic ambiguity of a specific localisation obtained by other methods. Nevertheless, obtaining good correspondence between two images with a high degree of dissimilarity is a complex task that may lead to important positioning errors. With the aim of increasing the accuracy with respect to the pair-wise image matching approaches, we present a new method to compute group-wise correspondences among a set of images. Thus, pair-wise errors are compensated and better correspondences between images are obtained. These correspondences can be used as a less-noisy input for the localisation process. Group-wise correspondences are computed by finding the common labelling of a set of salient points obtained from the images. Results show a clear increase in effectiveness with respect to methods that use only two images.-
dc.description.sponsorshipThis research is supported by “Consolider Ingenio 2010”: project CSD2007-00018, by the CICYT project DPI2010-17112 and by the Universitat Rovira I Virgili through a PhD research grant.-
dc.relation.isversionofPublisher's version-
dc.rightsopenAccess-
dc.subjectGroup wise point set alignment-
dc.subjectMultiple point set alignment-
dc.titleGroup-wise sparse correspondences between images based on a common labelling approach-
dc.typecomunicación de congreso-
dc.relation.publisherversionhttp://www.visapp.visigrapp.org/VISAPP2012/-
dc.date.updated2014-06-03T07:55:51Z-
dc.description.versionPeer Reviewed-
dc.language.rfc3066eng-
Appears in Collections:(IRII) Comunicaciones congresos
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