Home > Research > Publications & Outputs > Regularised estimation of 2D-locally stationary...

Research

Associated organisational units

Electronic data

  • 2dLSW_v6

    Rights statement: ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Accepted author manuscript, 946 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Regularised estimation of 2D-locally stationary wavelet processes

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

Regularised estimation of 2D-locally stationary wavelet processes. / Gibberd, A. J.; Nelson, J. D. B.
2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2016.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Gibberd, AJ & Nelson, JDB 2016, Regularised estimation of 2D-locally stationary wavelet processes. in 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE. https://doi.org/10.1109/SSP.2016.7551838

APA

Gibberd, A. J., & Nelson, J. D. B. (2016). Regularised estimation of 2D-locally stationary wavelet processes. In 2016 IEEE Statistical Signal Processing Workshop (SSP) IEEE. https://doi.org/10.1109/SSP.2016.7551838

Vancouver

Gibberd AJ, Nelson JDB. Regularised estimation of 2D-locally stationary wavelet processes. In 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE. 2016 doi: 10.1109/SSP.2016.7551838

Author

Gibberd, A. J. ; Nelson, J. D. B. / Regularised estimation of 2D-locally stationary wavelet processes. 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2016.

Bibtex

@inproceedings{969eb14c09634dcea4ca2660be204188,
title = "Regularised estimation of 2D-locally stationary wavelet processes",
abstract = "Locally Stationary Wavelet processes provide a flexible way of describing the time/space evolution of autocovariance structure over an ordered field such as an image/time-series.Classically, estimation of such models assume continuous smoothness of the underlying spectra and are estimated via local kernel smoothers. We propose a new model which permits spectral jumps, and suggest a regularised estimator and algorithm which can recover such structure from images. We demonstrate the effectiveness of our method in a synthetic experiment where it shows desirable estimation properties. We conclude with an application to real images which illustrate the qualitative difference between the proposed and previous methods.",
author = "Gibberd, {A. J.} and Nelson, {J. D. B.}",
note = "{\textcopyright}2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2016",
month = jun,
day = "1",
doi = "10.1109/SSP.2016.7551838",
language = "English",
isbn = "9781467378048",
booktitle = "2016 IEEE Statistical Signal Processing Workshop (SSP)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Regularised estimation of 2D-locally stationary wavelet processes

AU - Gibberd, A. J.

AU - Nelson, J. D. B.

N1 - ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2016/6/1

Y1 - 2016/6/1

N2 - Locally Stationary Wavelet processes provide a flexible way of describing the time/space evolution of autocovariance structure over an ordered field such as an image/time-series.Classically, estimation of such models assume continuous smoothness of the underlying spectra and are estimated via local kernel smoothers. We propose a new model which permits spectral jumps, and suggest a regularised estimator and algorithm which can recover such structure from images. We demonstrate the effectiveness of our method in a synthetic experiment where it shows desirable estimation properties. We conclude with an application to real images which illustrate the qualitative difference between the proposed and previous methods.

AB - Locally Stationary Wavelet processes provide a flexible way of describing the time/space evolution of autocovariance structure over an ordered field such as an image/time-series.Classically, estimation of such models assume continuous smoothness of the underlying spectra and are estimated via local kernel smoothers. We propose a new model which permits spectral jumps, and suggest a regularised estimator and algorithm which can recover such structure from images. We demonstrate the effectiveness of our method in a synthetic experiment where it shows desirable estimation properties. We conclude with an application to real images which illustrate the qualitative difference between the proposed and previous methods.

U2 - 10.1109/SSP.2016.7551838

DO - 10.1109/SSP.2016.7551838

M3 - Conference contribution/Paper

SN - 9781467378048

BT - 2016 IEEE Statistical Signal Processing Workshop (SSP)

PB - IEEE

ER -