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Locally stationary wavelet fields with application to the modelling and analysis of image texture.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Locally stationary wavelet fields with application to the modelling and analysis of image texture. / Eckley, Idris A.; Nason, Guy P.; Treloar, Robert L.
In: Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 59, No. 4, 08.2010, p. 595-616.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Eckley, IA, Nason, GP & Treloar, RL 2010, 'Locally stationary wavelet fields with application to the modelling and analysis of image texture.', Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 59, no. 4, pp. 595-616. https://doi.org/10.1111/j.1467-9876.2009.00721.x

APA

Eckley, I. A., Nason, G. P., & Treloar, R. L. (2010). Locally stationary wavelet fields with application to the modelling and analysis of image texture. Journal of the Royal Statistical Society: Series C (Applied Statistics), 59(4), 595-616. https://doi.org/10.1111/j.1467-9876.2009.00721.x

Vancouver

Eckley IA, Nason GP, Treloar RL. Locally stationary wavelet fields with application to the modelling and analysis of image texture. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2010 Aug;59(4):595-616. doi: 10.1111/j.1467-9876.2009.00721.x

Author

Eckley, Idris A. ; Nason, Guy P. ; Treloar, Robert L. / Locally stationary wavelet fields with application to the modelling and analysis of image texture. In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 2010 ; Vol. 59, No. 4. pp. 595-616.

Bibtex

@article{426e193bcc3f45498aa5d5244bac98dd,
title = "Locally stationary wavelet fields with application to the modelling and analysis of image texture.",
abstract = "This article proposes the modelling and analysis of image texture using an extension of a locally stationary wavelet process model into two-dimensions for lattice processes. Such a model permits construction of estimates of a spatially localized spectrum and localized autocovariance which can be used to characterize texture in a multiscale and spatially adaptive way. We provide the necessary theoretical support to show that our two-dimensional extension is properly defined and has the proper statistical convergence properties. Our use of a statistical model permits us to identify, and correct for, a bias in established texture measures based on non-decimated wavelet techniques. The proposed method performs nearly as well as optimal Fourier techniques on stationary textures and outperforms them in non-stationary situations. We illustrate our techniques using pilled fabric data from a fabric care experiment and simulated tile data.",
keywords = "random field, local spectrum, local autocovariance, texture classification, texture model, nondecimated wavelets",
author = "Eckley, {Idris A.} and Nason, {Guy P.} and Treloar, {Robert L.}",
year = "2010",
month = aug,
doi = "10.1111/j.1467-9876.2009.00721.x",
language = "English",
volume = "59",
pages = "595--616",
journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",
issn = "0035-9254",
publisher = "Wiley-Blackwell",
number = "4",

}

RIS

TY - JOUR

T1 - Locally stationary wavelet fields with application to the modelling and analysis of image texture.

AU - Eckley, Idris A.

AU - Nason, Guy P.

AU - Treloar, Robert L.

PY - 2010/8

Y1 - 2010/8

N2 - This article proposes the modelling and analysis of image texture using an extension of a locally stationary wavelet process model into two-dimensions for lattice processes. Such a model permits construction of estimates of a spatially localized spectrum and localized autocovariance which can be used to characterize texture in a multiscale and spatially adaptive way. We provide the necessary theoretical support to show that our two-dimensional extension is properly defined and has the proper statistical convergence properties. Our use of a statistical model permits us to identify, and correct for, a bias in established texture measures based on non-decimated wavelet techniques. The proposed method performs nearly as well as optimal Fourier techniques on stationary textures and outperforms them in non-stationary situations. We illustrate our techniques using pilled fabric data from a fabric care experiment and simulated tile data.

AB - This article proposes the modelling and analysis of image texture using an extension of a locally stationary wavelet process model into two-dimensions for lattice processes. Such a model permits construction of estimates of a spatially localized spectrum and localized autocovariance which can be used to characterize texture in a multiscale and spatially adaptive way. We provide the necessary theoretical support to show that our two-dimensional extension is properly defined and has the proper statistical convergence properties. Our use of a statistical model permits us to identify, and correct for, a bias in established texture measures based on non-decimated wavelet techniques. The proposed method performs nearly as well as optimal Fourier techniques on stationary textures and outperforms them in non-stationary situations. We illustrate our techniques using pilled fabric data from a fabric care experiment and simulated tile data.

KW - random field

KW - local spectrum

KW - local autocovariance

KW - texture classification

KW - texture model

KW - nondecimated wavelets

U2 - 10.1111/j.1467-9876.2009.00721.x

DO - 10.1111/j.1467-9876.2009.00721.x

M3 - Journal article

VL - 59

SP - 595

EP - 616

JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)

SN - 0035-9254

IS - 4

ER -