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Wavelet methods for the statistical analysis of image texture

Research output: ThesisDoctoral Thesis

Published

Standard

Wavelet methods for the statistical analysis of image texture. / Taylor, Sarah L.
2013. 191 p.

Research output: ThesisDoctoral Thesis

Harvard

Taylor, SL 2013, 'Wavelet methods for the statistical analysis of image texture', PhD, Lancaster University.

APA

Taylor, S. L. (2013). Wavelet methods for the statistical analysis of image texture. [Doctoral Thesis, Lancaster University].

Vancouver

Author

Bibtex

@phdthesis{0448ca9bf4b64c67a8f45a0a498ea9dc,
title = "Wavelet methods for the statistical analysis of image texture",
abstract = "This thesis considers the application of locally stationary wavelet-based stochastic modelsto the analysis of image texture. In the first part we propose a test of stationarity for spatialdata on a regular grid. This test is then incorporated into a segmentation framework inorder to determine the number of textures contained within an image, a key feature to manytexture segmentation approaches. These novel methods are subsequently applied to varioustexture analysis problems arising from work with an industrial collaborator. The secondpart of this thesis considers the modelling of the spectral structure of a non-stationarymultivariate image, i.e. an image containing different colour channels. We propose a multivariatelocally stationary wavelet-based modelling framework which permits a measure ofdependence between pairs of channels. The performance of this modelling approach is thenassessed using various colour texture examples encountered by an industrial collaborator.",
author = "Taylor, {Sarah L.}",
year = "2013",
month = jul,
language = "English",
school = "Lancaster University",

}

RIS

TY - BOOK

T1 - Wavelet methods for the statistical analysis of image texture

AU - Taylor, Sarah L.

PY - 2013/7

Y1 - 2013/7

N2 - This thesis considers the application of locally stationary wavelet-based stochastic modelsto the analysis of image texture. In the first part we propose a test of stationarity for spatialdata on a regular grid. This test is then incorporated into a segmentation framework inorder to determine the number of textures contained within an image, a key feature to manytexture segmentation approaches. These novel methods are subsequently applied to varioustexture analysis problems arising from work with an industrial collaborator. The secondpart of this thesis considers the modelling of the spectral structure of a non-stationarymultivariate image, i.e. an image containing different colour channels. We propose a multivariatelocally stationary wavelet-based modelling framework which permits a measure ofdependence between pairs of channels. The performance of this modelling approach is thenassessed using various colour texture examples encountered by an industrial collaborator.

AB - This thesis considers the application of locally stationary wavelet-based stochastic modelsto the analysis of image texture. In the first part we propose a test of stationarity for spatialdata on a regular grid. This test is then incorporated into a segmentation framework inorder to determine the number of textures contained within an image, a key feature to manytexture segmentation approaches. These novel methods are subsequently applied to varioustexture analysis problems arising from work with an industrial collaborator. The secondpart of this thesis considers the modelling of the spectral structure of a non-stationarymultivariate image, i.e. an image containing different colour channels. We propose a multivariatelocally stationary wavelet-based modelling framework which permits a measure ofdependence between pairs of channels. The performance of this modelling approach is thenassessed using various colour texture examples encountered by an industrial collaborator.

M3 - Doctoral Thesis

ER -