Home > Research > Publications & Outputs > Multivariate locally stationary 2D wavelet proc...

Research

Associated organisational units

Links

Text available via DOI:

Keywords

View graph of relations

Multivariate locally stationary 2D wavelet processes with application to colour texture analysis

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

Published

Standard

Multivariate locally stationary 2D wavelet processes with application to colour texture analysis. / Taylor, Sarah; Eckley, Idris Arthur; Nunes, Matthew Alan.
In: Statistics and Computing, Vol. 27, No. 4, 07.2017, p. 1129-1143.

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

Harvard

Taylor, S, Eckley, IA & Nunes, MA 2017, 'Multivariate locally stationary 2D wavelet processes with application to colour texture analysis', Statistics and Computing, vol. 27, no. 4, pp. 1129-1143. https://doi.org/10.1007/s11222-016-9675-9

APA

Taylor, S., Eckley, I. A., & Nunes, M. A. (2017). Multivariate locally stationary 2D wavelet processes with application to colour texture analysis. Statistics and Computing, 27(4), 1129-1143. https://doi.org/10.1007/s11222-016-9675-9

Vancouver

Taylor S, Eckley IA, Nunes MA. Multivariate locally stationary 2D wavelet processes with application to colour texture analysis. Statistics and Computing. 2017 Jul;27(4):1129-1143. Epub 2016 Jul 1. doi: 10.1007/s11222-016-9675-9

Author

Taylor, Sarah ; Eckley, Idris Arthur ; Nunes, Matthew Alan. / Multivariate locally stationary 2D wavelet processes with application to colour texture analysis. In: Statistics and Computing. 2017 ; Vol. 27, No. 4. pp. 1129-1143.

Bibtex

@article{1650fb45e6db4eb99278aea39fa8b32a,
title = "Multivariate locally stationary 2D wavelet processes with application to colour texture analysis",
abstract = "In this article we propose a novel framework for the modelling of non-stationary multivariate lattice processes. Our approach extends the locally stationary wavelet paradigm into the multivariate two-dimensional setting. As such the framework we develop permits the estimation of a spatially localised spectrum within a channel of interest and, more importantly, a localised cross-covariance which describes the localised coherence between channels. Associated estimation theory is also established which demonstrates that this multivariate spatial framework is properly defined and has suitable convergence properties. We also demonstrate how this model-based approach can be successfully used to classify a range of colour textures provided by an industrial collaborator, yielding superior results when compared against current state-of-the-art statistical image processing methods.",
keywords = "Random field, Local spectrum, Local coherence , Colour texture , Wavelets",
author = "Sarah Taylor and Eckley, {Idris Arthur} and Nunes, {Matthew Alan}",
year = "2017",
month = jul,
doi = "10.1007/s11222-016-9675-9",
language = "English",
volume = "27",
pages = "1129--1143",
journal = "Statistics and Computing",
issn = "0960-3174",
publisher = "Springer Netherlands",
number = "4",

}

RIS

TY - JOUR

T1 - Multivariate locally stationary 2D wavelet processes with application to colour texture analysis

AU - Taylor, Sarah

AU - Eckley, Idris Arthur

AU - Nunes, Matthew Alan

PY - 2017/7

Y1 - 2017/7

N2 - In this article we propose a novel framework for the modelling of non-stationary multivariate lattice processes. Our approach extends the locally stationary wavelet paradigm into the multivariate two-dimensional setting. As such the framework we develop permits the estimation of a spatially localised spectrum within a channel of interest and, more importantly, a localised cross-covariance which describes the localised coherence between channels. Associated estimation theory is also established which demonstrates that this multivariate spatial framework is properly defined and has suitable convergence properties. We also demonstrate how this model-based approach can be successfully used to classify a range of colour textures provided by an industrial collaborator, yielding superior results when compared against current state-of-the-art statistical image processing methods.

AB - In this article we propose a novel framework for the modelling of non-stationary multivariate lattice processes. Our approach extends the locally stationary wavelet paradigm into the multivariate two-dimensional setting. As such the framework we develop permits the estimation of a spatially localised spectrum within a channel of interest and, more importantly, a localised cross-covariance which describes the localised coherence between channels. Associated estimation theory is also established which demonstrates that this multivariate spatial framework is properly defined and has suitable convergence properties. We also demonstrate how this model-based approach can be successfully used to classify a range of colour textures provided by an industrial collaborator, yielding superior results when compared against current state-of-the-art statistical image processing methods.

KW - Random field

KW - Local spectrum

KW - Local coherence

KW - Colour texture

KW - Wavelets

U2 - 10.1007/s11222-016-9675-9

DO - 10.1007/s11222-016-9675-9

M3 - Journal article

VL - 27

SP - 1129

EP - 1143

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

IS - 4

ER -