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Markov random field image modelling
Includes bibliographical references.
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| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Electrical Engineering
2014
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| _version_ | 1867613337739591680 |
|---|---|
| access_status_str | Open Access |
| author | McGrath, Michael |
| author2 | De Jager, Gerhard |
| author_browse | De Jager, Gerhard McGrath, Michael |
| author_facet | De Jager, Gerhard McGrath, Michael |
| author_sort | McGrath, Michael |
| collection | Thesis |
| description | Includes bibliographical references. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/5166 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:34:32.198Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/5166 Markov random field image modelling McGrath, Michael De Jager, Gerhard Electrical Engineering Includes bibliographical references. This work investigated some of the consequences of using a priori information in image processing using computer tomography (CT) as an example. Prior information is information about the solution that is known apart from measurement data. This information can be represented as a probability distribution. In order to define a probability density distribution in high dimensional problems like those found in image processing it becomes necessary to adopt some form of parametric model for the distribution. Markov random fields (MRFs) provide just such a vehicle for modelling the a priori distribution of labels found in images. In particular, this work investigated the suitability of MRF models for modelling a priori information about the distribution of attenuation coefficients found in CT scans. 2014-07-31T10:54:49Z 2014-07-31T10:54:49Z 2003 Master Thesis Masters MSc http://hdl.handle.net/11427/5166 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical Engineering McGrath, Michael Markov random field image modelling |
| thesis_degree_str | Master's |
| title | Markov random field image modelling |
| title_full | Markov random field image modelling |
| title_fullStr | Markov random field image modelling |
| title_full_unstemmed | Markov random field image modelling |
| title_short | Markov random field image modelling |
| title_sort | markov random field image modelling |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/5166 |
| work_keys_str_mv | AT mcgrathmichael markovrandomfieldimagemodelling |