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LS2W: implementing the locally stationary 2D wavelet process approach in R.

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LS2W: implementing the locally stationary 2D wavelet process approach in R. / Eckley, Idris A.; Nason, Guy P.

In: Journal of Statistical Software, Vol. 43, No. 3, 07.2011, p. 1-23.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Eckley, IA & Nason, GP 2011, 'LS2W: implementing the locally stationary 2D wavelet process approach in R.', Journal of Statistical Software, vol. 43, no. 3, pp. 1-23. <http://www.jstatsoft.org/v43/i03>

APA

Vancouver

Eckley IA, Nason GP. LS2W: implementing the locally stationary 2D wavelet process approach in R. Journal of Statistical Software. 2011 Jul;43(3):1-23.

Author

Eckley, Idris A. ; Nason, Guy P. / LS2W: implementing the locally stationary 2D wavelet process approach in R. In: Journal of Statistical Software. 2011 ; Vol. 43, No. 3. pp. 1-23.

Bibtex

@article{5b10c7dd8521418e9cc97ae3638689ba,
title = "LS2W: implementing the locally stationary 2D wavelet process approach in R.",
abstract = "Locally stationary process representations have recently been proposed and applied to both time series and image analysis applications. This article describes an implementation of the locally stationary two-dimensional wavelet (LS2W) process approach in R. This package permits construction of estimates of spatially localized spectra and localized autocovariance which can be used to characterize structure within images.",
keywords = "random field, locally stationary, local autocovariance, LS2W, texture analysis, non-decimated wavelets, R.",
author = "Eckley, {Idris A.} and Nason, {Guy P.}",
year = "2011",
month = jul,
language = "English",
volume = "43",
pages = "1--23",
journal = "Journal of Statistical Software",
issn = "1548-7660",
publisher = "University of California at Los Angeles",
number = "3",

}

RIS

TY - JOUR

T1 - LS2W: implementing the locally stationary 2D wavelet process approach in R.

AU - Eckley, Idris A.

AU - Nason, Guy P.

PY - 2011/7

Y1 - 2011/7

N2 - Locally stationary process representations have recently been proposed and applied to both time series and image analysis applications. This article describes an implementation of the locally stationary two-dimensional wavelet (LS2W) process approach in R. This package permits construction of estimates of spatially localized spectra and localized autocovariance which can be used to characterize structure within images.

AB - Locally stationary process representations have recently been proposed and applied to both time series and image analysis applications. This article describes an implementation of the locally stationary two-dimensional wavelet (LS2W) process approach in R. This package permits construction of estimates of spatially localized spectra and localized autocovariance which can be used to characterize structure within images.

KW - random field

KW - locally stationary

KW - local autocovariance

KW - LS2W

KW - texture analysis

KW - non-decimated wavelets

KW - R.

M3 - Journal article

VL - 43

SP - 1

EP - 23

JO - Journal of Statistical Software

JF - Journal of Statistical Software

SN - 1548-7660

IS - 3

ER -