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On the analysis of structure in texture

On the analysis of structure in texture
On the analysis of structure in texture
Until now texture has been largely viewed as a statistical or holistic paradigm: textures are described as a whole and by summary statistics. In this thesis it is assumed that there is a structure underlying the texture leading to models, reconstruction and to scale based analysis. Local Binary Patterns are used throughout as the basis functions for texture and methods have been developed to reconstruct texture images from arrays of their LBP codes. The reconstructed images contain identical texture properties to the original; providing the same array of LBP codes. An evidence gathering approach has been developed to provide a model for each texture class based on the spatial structure of these elements throughout the image. This method, called Evidence Gathering Texture Segmentation, provides good results for segmentation with smooth boundaries and minimal oversegmentation, when compared with existing methods. Analysing microand macro-structures confers ability to include scale in texture analysis. A novel combination of lowpass and highpass filters produces images devoid of structures at certain scales; allowing both the micro- and macro-structures to be analysed without occlusion by other scales of texture within the image. A two stage training process is used to learn the optimum filter sizes and to produce model histograms for each known texture class. The process, called Accumulative Filtering, gives superior results compared to the best multiresolution LBP configuration and analysis only using lowpass filters. By reconstruction, by evidence gathering and by analysis of micro- and macro-structures, new capabilities are described to exploit structure within the analysis of texture.
Waller, Ben
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Waller, Ben
a802857c-78ee-4290-a400-20d9ef2b0f5e
Nixon, Mark
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Waller, Ben (2014) On the analysis of structure in texture. University of Southampton, Physical and Sciences and Engineering, Doctoral Thesis, 144pp.

Record type: Thesis (Doctoral)

Abstract

Until now texture has been largely viewed as a statistical or holistic paradigm: textures are described as a whole and by summary statistics. In this thesis it is assumed that there is a structure underlying the texture leading to models, reconstruction and to scale based analysis. Local Binary Patterns are used throughout as the basis functions for texture and methods have been developed to reconstruct texture images from arrays of their LBP codes. The reconstructed images contain identical texture properties to the original; providing the same array of LBP codes. An evidence gathering approach has been developed to provide a model for each texture class based on the spatial structure of these elements throughout the image. This method, called Evidence Gathering Texture Segmentation, provides good results for segmentation with smooth boundaries and minimal oversegmentation, when compared with existing methods. Analysing microand macro-structures confers ability to include scale in texture analysis. A novel combination of lowpass and highpass filters produces images devoid of structures at certain scales; allowing both the micro- and macro-structures to be analysed without occlusion by other scales of texture within the image. A two stage training process is used to learn the optimum filter sizes and to produce model histograms for each known texture class. The process, called Accumulative Filtering, gives superior results compared to the best multiresolution LBP configuration and analysis only using lowpass filters. By reconstruction, by evidence gathering and by analysis of micro- and macro-structures, new capabilities are described to exploit structure within the analysis of texture.

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Published date: March 2014
Organisations: University of Southampton, Southampton Wireless Group

Identifiers

Local EPrints ID: 364755
URI: http://eprints.soton.ac.uk/id/eprint/364755
PURE UUID: f70f68f6-f257-47c4-8f86-97db65a649c4
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 02 Jun 2014 10:52
Last modified: 06 Jun 2018 13:18

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