TY - GEN
T1 - Image analysis based on an improved bidimensional empirical mode decomposition method
AU - Zhang, Dan
AU - Pan, Jianjia
AU - Tang, Yuan Yan
N1 - Publisher copyright:
© 2010 IEEE
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 - http://www.scopus.com/inward/record.url?scp=77958179756&partnerID=8YFLogxK
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 -
