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Texture measures for segmentation
Includes bibliographical references (p. 67-72).
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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_ | 1867613243362508800 |
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| access_status_str | Open Access |
| author | Haddad, Stephen |
| author2 | Nicolls, Fred |
| author_browse | Haddad, Stephen Nicolls, Fred |
| author_facet | Nicolls, Fred Haddad, Stephen |
| author_sort | Haddad, Stephen |
| collection | Thesis |
| description | Includes bibliographical references (p. 67-72). |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/7461 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:01.081Z |
| 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:11427/7461 Texture measures for segmentation Haddad, Stephen Nicolls, Fred Electrical Engineering Includes bibliographical references (p. 67-72). Texture is an important visual cue in both human and computer vision. Segmenting images into regions of constant texture is used in many applications. This work surveys a wide range of texture descriptors and segmentation methods to determine the state of the art in texture segmentation. Two types of texture descriptors are investigated: filter bank based methods and local descriptors. Filter banks deconstruct an image into several bands, each of which emphasises areas of the image with different properties. Textons are an adaptive histogram method which describes the distribution of typical feature vectors. Local descriptors calculate features from smaller neighbourhoods than filter banks. Some local descriptors calculate a scale for their local neighbourhood to achieve scale invariance. Both local and global segmentation methods are investigated. Local segmentation methods consider each pixel in isolation. Global segmentation methods penalise jagged borders or fragmented regions in the segmentation. Pixel labelling and border detection methods are investigated. Methods for measuring the accuracy of segmentation are discussed. Two data sets are used to test the texture segmentation algorithms. The Brodatz Album mosaics are composed of grayscale texture images from the Brodatz Album. The Berkeley Natural Images data set has 300 colour images of natural scenes. The tests show that, of the descriptors tested, filter bank based textons are the best texture descriptors for grayscale images. Local image patch textons are best for colour images. Graph cut segmentation is best for pixel labelling problems and edge detection with regular borders. Non-maxima suppression is best for edge detection with irregular borders. Factors affecting the performance of the algorithms are investigated. 2014-09-15T07:24:50Z 2014-09-15T07:24:50Z 2007 Master Thesis Masters MSc http://hdl.handle.net/11427/7461 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical Engineering Haddad, Stephen Texture measures for segmentation |
| thesis_degree_str | Master's |
| title | Texture measures for segmentation |
| title_full | Texture measures for segmentation |
| title_fullStr | Texture measures for segmentation |
| title_full_unstemmed | Texture measures for segmentation |
| title_short | Texture measures for segmentation |
| title_sort | texture measures for segmentation |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/7461 |
| work_keys_str_mv | AT haddadstephen texturemeasuresforsegmentation |