Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/11084
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dc.contributor.authorGerogiannis, Demetriosen
dc.contributor.authorNikou, Christophorosen
dc.contributor.authorLikas, Aristidisen
dc.date.accessioned2015-11-24T17:02:41Z-
dc.date.available2015-11-24T17:02:41Z-
dc.identifier.issn0925-2312-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/11084-
dc.rightsDefault Licence-
dc.subjectRegistration of sets of pointsen
dc.subjectRelevance vector machine (RVM)en
dc.subjectHungarian algorithmen
dc.titleRegistering sets of points using Bayesian regressionen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primary10.1016/j.neucom.2012.02.018-
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.publicationDate2012-
heal.abstractThis work addresses the problem of non-rigid registration between two 2D or 3D points sets as a novel application of Relevance Vector Machines (RVM). An iterative framework is proposed which consists of two steps: at first, correspondences between distinct points are estimated by the Hungarian algorithm and then a regression procedure based on a Bayesian linear model (RVM) maps the two sets of points. By these means, a large variety of transformation is captured without imposing any prior knowledge on the form of the point sets. The proposed algorithm provides a smooth transformation even if the correspondence between the points in the two sets contains erroneous matches. The algorithm was successfully evaluated on sets of points with varying difficulty and favorably compared with state-of-the-art methods in cases of noise.en
heal.journalNameNeurocomputingen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

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