Adelaide Research & Scholarship
Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/103974
| Citations | ||
| Scopus | Web of Science® | Altmetric |
|---|---|---|
|
?
|
?
|
|
Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Chin, Tat-Jun | - |
| dc.contributor.advisor | Suter, David | - |
| dc.contributor.author | Kee, Yang Heng | - |
| dc.date.issued | 2016 | - |
| dc.identifier.uri | http://hdl.handle.net/2440/103974 | - |
| dc.description.abstract | Maximum 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.subject | computer vision | en |
| dc.subject | maximum consensus | en |
| dc.subject | parameter estimation | en |
| dc.subject | mixed integer linear programming | en |
| dc.title | Maximum consensus with mixed integer linear programming | en |
| dc.type | Theses | en |
| dc.contributor.school | School of Computer Science | en |
| dc.provenance | This 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/legals | en |
| dc.description.dissertation | Thesis (M.Phil.) -- University of Adelaide, School of Computer Science, 2016. | en |
| dc.identifier.doi | 10.4225/55/58d219e9ecb9f | - |
| Appears in Collections: | Research Theses | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 01front.pdf | 144.96 kB | Adobe PDF | View/Open | |
| 02whole.pdf | 10.41 MB | Adobe PDF | View/Open | |
| Permissions Restricted Access | Library staff access only | 229.05 kB | Adobe PDF | View/Open |
| Restricted Restricted Access | Library staff access only | 10.99 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.