Maximum consensus with mixed integer linear programming

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dc.contributor.advisorChin, Tat-Jun
dc.contributor.advisorSuter, David
dc.contributor.authorKee, Yang Heng
dc.contributor.schoolSchool of Computer Scienceen
dc.date.issued2016
dc.description.abstractMaximum consensus is fundamentally important in computer vision as a criterion for robust estimation, where the goal is to estimate the parameters of a model of best fit. It is computationally demanding to solve such problems exactly. Instead, conventional methods employ randomised sample-and-test techniques to approximate a solution, which fail to guarantee the optimality of the result. This thesis makes several contributions towards solving the maximum consensus problem exactly in the context of Mixed Integer Linear Programming. In particular, two preprocessing techniques aimed at improving the speed and efficiency of exact methods are proposed.en
dc.description.dissertationThesis (M.Phil.) -- University of Adelaide, School of Computer Science, 2016.en
dc.identifier.doi10.4225/55/58d219e9ecb9f
dc.identifier.urihttp://hdl.handle.net/2440/103974
dc.provenanceThis electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legalsen
dc.subjectcomputer visionen
dc.subjectmaximum consensusen
dc.subjectparameter estimationen
dc.subjectmixed integer linear programmingen
dc.titleMaximum consensus with mixed integer linear programmingen
dc.typeThesesen

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