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dc.contributor.authorMazumder, Joyen
dc.date.accessioned2020-09-21T19:38:15Z
dc.date.available2020-09-21T19:38:15Z
dc.identifier.urihttp://hdl.handle.net/1974/28131
dc.description.abstractThis thesis proposes a novel deep learning architecture called ValidNet to automatically validate the correctness of a 3D surface registration outcome. The performance of many tasks such as object detection mainly depends on the applied registration algorithms, which themselves are susceptible to local minima. Revealing this tendency and verifying the success of registration algorithms is a difficult task. We treat this as a classification problem, and propose a two-class classifier to distinguish clearly between true positive and false positive registration outcomes. Our proposed ValidNet system deploys a shared multilayer perceptron architecture which works on the raw and unordered point cloud data of scene and model points. This network is able to perform the two fundamental tasks of feature extraction and similarity matching using the powerful capability of a deep neural network. Experiments on a large synthetic dataset show that the proposed method can effectively be used in automatic validation of 3D surface registration.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.rightsAttribution-NonCommercial 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/us/*
dc.subjectSurface Registrationen
dc.subject3D Object Recognitionen
dc.subjectValidationen
dc.subjectShared mlp Networken
dc.subjectPoint Clouden
dc.titleA Deep Learning Network for Validation of 3D Point Cloud Surface Registrationen
dc.typethesisen
dc.description.degreeM.A.Sc.en
dc.contributor.supervisorGreenspan, Michael
dc.contributor.departmentElectrical and Computer Engineeringen
dc.degree.grantorQueen's University at Kingstonen


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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
Except where otherwise noted, this item's license is described as Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada