Image analysis based on an improved bidimensional empirical mode decomposition method

Dan Zhang; Jianjia Pan; Yuan Yan Tang

doi:10.1109/ICWAPR.2010.5576310

Image analysis based on an improved bidimensional empirical mode decomposition method

Dan Zhang^*
, Jianjia Pan
, Yuan Yan Tang

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

Abstract

The Empirical Mode Decomposition (EMD) is a new adaptive signal decomposition method, which is good at handling many real nonlinear and nonstationary one dimensional signals. It decomposes signals into a a series of Intrinsic Mode Functions (IMFs) that was shown having better behaved instantaneous frequencies via Hilbert transform (The EMD and Hilbert spectrum analysis together were called Hilbert-Huang Transform (HHT) which was proposed by N.E.Huang et at. in [5].). For the advanced applications in image analysis, the EMD was extended to the bidimensional EMD (BEMD). However, most of the existed BEMD algorithms are slow and have unsatisfied results. In this paper, we firstly proposed a new BEMD algorithm which is comparatively faster and better-performed. Then we use the Riesz transform to get the monogenic signals. The local features (amplitude, phase orientation, phase angle, etc) are evaluated. The simulation results are given in the experiments.

Original language	English
Title of host publication	Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2010
Publisher	IEEE
Pages	144-149
Number of pages	6
ISBN (Electronic)	9781424465316, 9781424465293
ISBN (Print)	9781424465309
DOIs	https://doi.org/10.1109/ICWAPR.2010.5576310
Publication status	Published - 11 Jul 2010
Event	2010 8th International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2010 - Qingdao, China Duration: 11 Jul 2010 → 14 Jul 2010

Publication series

Name	International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR
Publisher	IEEE
ISSN (Print)	2158-5695
ISSN (Electronic)	2158-5709

Conference

Conference	2010 8th International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2010
Country/Territory	China
City	Qingdao
Period	11/07/10 → 14/07/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

User-Defined Keywords

Bidimensional empirical mode decomposition
Hilbert huang transform
Image analysis

Access to Document

10.1109/ICWAPR.2010.5576310

Cite this

Zhang, D., Pan, J., & Tang, Y. Y. (2010). Image analysis based on an improved bidimensional empirical mode decomposition method. In Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2010 (pp. 144-149). (International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR). IEEE. https://doi.org/10.1109/ICWAPR.2010.5576310

@inproceedings{e8a9f4f3465e4335b2554a71d6eff5fd,

title = "Image analysis based on an improved bidimensional empirical mode decomposition method",

abstract = "The Empirical Mode Decomposition (EMD) is a new adaptive signal decomposition method, which is good at handling many real nonlinear and nonstationary one dimensional signals. It decomposes signals into a a series of Intrinsic Mode Functions (IMFs) that was shown having better behaved instantaneous frequencies via Hilbert transform (The EMD and Hilbert spectrum analysis together were called Hilbert-Huang Transform (HHT) which was proposed by N.E.Huang et at. in [5].). For the advanced applications in image analysis, the EMD was extended to the bidimensional EMD (BEMD). However, most of the existed BEMD algorithms are slow and have unsatisfied results. In this paper, we firstly proposed a new BEMD algorithm which is comparatively faster and better-performed. Then we use the Riesz transform to get the monogenic signals. The local features (amplitude, phase orientation, phase angle, etc) are evaluated. The simulation results are given in the experiments.",

keywords = "Bidimensional empirical mode decomposition, Hilbert huang transform, Image analysis",

author = "Dan Zhang and Jianjia Pan and Tang, \{Yuan Yan\}",

note = "Publisher copyright: {\textcopyright} 2010 IEEE; 2010 8th International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2010 ; Conference date: 11-07-2010 Through 14-07-2010",

year = "2010",

month = jul,

day = "11",

doi = "10.1109/ICWAPR.2010.5576310",

language = "English",

isbn = "9781424465309",

series = "International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR",

publisher = "IEEE",

pages = "144--149",

booktitle = "Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2010",

address = "United States",

}

Zhang, D, Pan, J & Tang, YY 2010, Image analysis based on an improved bidimensional empirical mode decomposition method. in Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2010. International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR, IEEE, pp. 144-149, 2010 8th International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2010, Qingdao, China, 11/07/10. https://doi.org/10.1109/ICWAPR.2010.5576310

Image analysis based on an improved bidimensional empirical mode decomposition method. / Zhang, Dan; Pan, Jianjia; Tang, Yuan Yan.
Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2010. IEEE, 2010. p. 144-149 (International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Image analysis based on an improved bidimensional empirical mode decomposition method

AU - Zhang, Dan

AU - Pan, Jianjia

AU - Tang, Yuan Yan

PY - 2010/7/11

Y1 - 2010/7/11

N2 - The Empirical Mode Decomposition (EMD) is a new adaptive signal decomposition method, which is good at handling many real nonlinear and nonstationary one dimensional signals. It decomposes signals into a a series of Intrinsic Mode Functions (IMFs) that was shown having better behaved instantaneous frequencies via Hilbert transform (The EMD and Hilbert spectrum analysis together were called Hilbert-Huang Transform (HHT) which was proposed by N.E.Huang et at. in [5].). For the advanced applications in image analysis, the EMD was extended to the bidimensional EMD (BEMD). However, most of the existed BEMD algorithms are slow and have unsatisfied results. In this paper, we firstly proposed a new BEMD algorithm which is comparatively faster and better-performed. Then we use the Riesz transform to get the monogenic signals. The local features (amplitude, phase orientation, phase angle, etc) are evaluated. The simulation results are given in the experiments.

AB - The Empirical Mode Decomposition (EMD) is a new adaptive signal decomposition method, which is good at handling many real nonlinear and nonstationary one dimensional signals. It decomposes signals into a a series of Intrinsic Mode Functions (IMFs) that was shown having better behaved instantaneous frequencies via Hilbert transform (The EMD and Hilbert spectrum analysis together were called Hilbert-Huang Transform (HHT) which was proposed by N.E.Huang et at. in [5].). For the advanced applications in image analysis, the EMD was extended to the bidimensional EMD (BEMD). However, most of the existed BEMD algorithms are slow and have unsatisfied results. In this paper, we firstly proposed a new BEMD algorithm which is comparatively faster and better-performed. Then we use the Riesz transform to get the monogenic signals. The local features (amplitude, phase orientation, phase angle, etc) are evaluated. The simulation results are given in the experiments.

KW - Bidimensional empirical mode decomposition

KW - Hilbert huang transform

KW - Image analysis

UR - https://www.scopus.com/pages/publications/77958179756

U2 - 10.1109/ICWAPR.2010.5576310

DO - 10.1109/ICWAPR.2010.5576310

M3 - Conference proceeding

AN - SCOPUS:77958179756

SN - 9781424465309

T3 - International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR

SP - 144

EP - 149

BT - Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2010

PB - IEEE

T2 - 2010 8th International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2010

Y2 - 11 July 2010 through 14 July 2010

ER -

Image analysis based on an improved bidimensional empirical mode decomposition method

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