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    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.

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Introducing the locally stationary dual-tree complex wavelet model

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

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Introducing the locally stationary dual-tree complex wavelet model. / Nelson, J. D. B.; Gibberd, A. J.
2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. p. 3583-3587.

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

Harvard

Nelson, JDB & Gibberd, AJ 2016, Introducing the locally stationary dual-tree complex wavelet model. in 2016 IEEE International Conference on Image Processing (ICIP). IEEE, pp. 3583-3587, 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, Arizona, United States, 25/09/16. https://doi.org/10.1109/ICIP.2016.7533027

APA

Nelson, J. D. B., & Gibberd, A. J. (2016). Introducing the locally stationary dual-tree complex wavelet model. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 3583-3587). IEEE. https://doi.org/10.1109/ICIP.2016.7533027

Vancouver

Nelson JDB, Gibberd AJ. Introducing the locally stationary dual-tree complex wavelet model. In 2016 IEEE International Conference on Image Processing (ICIP). IEEE. 2016. p. 3583-3587 Epub 2016 Aug 19. doi: 10.1109/ICIP.2016.7533027

Author

Nelson, J. D. B. ; Gibberd, A. J. / Introducing the locally stationary dual-tree complex wavelet model. 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. pp. 3583-3587

Bibtex

@inproceedings{793565b22bc54cbf99d1aefc90d3124a,
title = "Introducing the locally stationary dual-tree complex wavelet model",
abstract = "This paper reconciles Kingsbury's dual-tree complex wavelets with Nason and Eckley's locally stationary model. We here establish that the dual-tree wavelets admit an invertible de-biasing matrix and that this matrix can be used to invert the covariance relation. We also show that the added directional selectivity of the proposed model adds utility to the standard two-dimensional local stationary model. Non-stationarity detection on random fields is used as a motivating example. Experiments confirm that the dual-tree model can distinguish anisotropic non-stationarities significantly better than the current model.",
author = "Nelson, {J. D. B.} and Gibberd, {A. J.}",
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.; 2016 IEEE International Conference on Image Processing (ICIP) ; Conference date: 25-09-2016 Through 28-09-2016",
year = "2016",
month = sep,
day = "28",
doi = "10.1109/ICIP.2016.7533027",
language = "English",
isbn = "9781467399623",
pages = "3583--3587",
booktitle = "2016 IEEE International Conference on Image Processing (ICIP)",
publisher = "IEEE",
url = "https://ieeexplore.ieee.org/xpl/conhome/7527113/proceeding",

}

RIS

TY - GEN

T1 - Introducing the locally stationary dual-tree complex wavelet model

AU - Nelson, J. D. B.

AU - Gibberd, A. J.

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 - 2016/9/28

Y1 - 2016/9/28

N2 - This paper reconciles Kingsbury's dual-tree complex wavelets with Nason and Eckley's locally stationary model. We here establish that the dual-tree wavelets admit an invertible de-biasing matrix and that this matrix can be used to invert the covariance relation. We also show that the added directional selectivity of the proposed model adds utility to the standard two-dimensional local stationary model. Non-stationarity detection on random fields is used as a motivating example. Experiments confirm that the dual-tree model can distinguish anisotropic non-stationarities significantly better than the current model.

AB - This paper reconciles Kingsbury's dual-tree complex wavelets with Nason and Eckley's locally stationary model. We here establish that the dual-tree wavelets admit an invertible de-biasing matrix and that this matrix can be used to invert the covariance relation. We also show that the added directional selectivity of the proposed model adds utility to the standard two-dimensional local stationary model. Non-stationarity detection on random fields is used as a motivating example. Experiments confirm that the dual-tree model can distinguish anisotropic non-stationarities significantly better than the current model.

U2 - 10.1109/ICIP.2016.7533027

DO - 10.1109/ICIP.2016.7533027

M3 - Conference contribution/Paper

SN - 9781467399623

SP - 3583

EP - 3587

BT - 2016 IEEE International Conference on Image Processing (ICIP)

PB - IEEE

T2 - 2016 IEEE International Conference on Image Processing (ICIP)

Y2 - 25 September 2016 through 28 September 2016

ER -