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Analysing Micro- and Macro-Structures in Textures

Analysing Micro- and Macro-Structures in Textures
Analysing Micro- and Macro-Structures in Textures
Analysing micro- and macro-structures within images confers ability to include scale in texture analysis. Filtering allows for selection of texture structures at different scales, revealing the micro- and macro-structures which would otherwise be concealed. The new approach to texture segmentation uses low- and high-pass filters to achieve this scale-based analysis. Segmentation is performed using Local Binary Patterns as an example of the type of feature vector that can be used with the new process. These are generated for the original image and each of the filtered images. A two stage training process is used to learn the optimum filter sizes and to produce model histograms for each known texture class. These are used in the supervised segmentation of texture mosaics generated from the VisTex database. The results demonstrate the superiority of the new combined approach compared to the best multi-resolution LBP configuration and analysis only using lowpass filters. Noise analysis has also confirmed the advantageous properties of low- and high-pass filtering, and confirms that it is optimal to combine the two forms in texture segmentation.
Waller, Ben M.
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Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da
Waller, Ben M.
4ce443a8-8c74-4425-8f25-93dd73a2fde5
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da

Waller, Ben M., Nixon, Mark S. and Carter, John N. (2012) Analysing Micro- and Macro-Structures in Textures. 8th International Conference on Signal Image Technology and Internet Based Systems, Italy. 25 - 29 Nov 2012. 8 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Analysing micro- and macro-structures within images confers ability to include scale in texture analysis. Filtering allows for selection of texture structures at different scales, revealing the micro- and macro-structures which would otherwise be concealed. The new approach to texture segmentation uses low- and high-pass filters to achieve this scale-based analysis. Segmentation is performed using Local Binary Patterns as an example of the type of feature vector that can be used with the new process. These are generated for the original image and each of the filtered images. A two stage training process is used to learn the optimum filter sizes and to produce model histograms for each known texture class. These are used in the supervised segmentation of texture mosaics generated from the VisTex database. The results demonstrate the superiority of the new combined approach compared to the best multi-resolution LBP configuration and analysis only using lowpass filters. Noise analysis has also confirmed the advantageous properties of low- and high-pass filtering, and confirms that it is optimal to combine the two forms in texture segmentation.

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More information

Published date: 28 November 2012
Venue - Dates: 8th International Conference on Signal Image Technology and Internet Based Systems, Italy, 2012-11-25 - 2012-11-29
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 349017
URI: https://eprints.soton.ac.uk/id/eprint/349017
PURE UUID: 076a38ee-f8b6-402d-aee6-7cb396caa3c3
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 25 Feb 2013 15:03
Last modified: 20 Jul 2019 01:28

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