Fringe-projection profilometry based on two-dimensional empirical mode decomposition

2013 ◽  
Vol 52 (31) ◽  
pp. 7648 ◽  
Author(s):  
Suzhen Zheng ◽  
Yiping Cao
Keyword(s):  
Empirical Mode Decomposition ◽  
Fringe Projection ◽  
Two Dimensional ◽  
Mode Decomposition ◽  
Fringe Projection Profilometry

Two-dimensional empirical mode decomposition by finite elements

2006 ◽  
Vol 462 (2074) ◽  
pp. 3081-3096 ◽  
Author(s):  
Y Xu ◽  
B Liu ◽  
J Liu ◽  
S Riemenschneider
Keyword(s):  
Finite Element ◽  
Linear Combination ◽  
Empirical Mode Decomposition ◽  
Spline Interpolation ◽  
Basis Functions ◽  
Two Dimensional ◽  
Element Analysis ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Mode Decomposition

Empirical mode decomposition (EMD) is a powerful tool for analysis of non-stationary and nonlinear signals, and has drawn significant attention in various engineering application areas. This paper presents a finite element-based EMD method for two-dimensional data analysis. Specifically, we represent the local mean surface of the data, a key step in EMD, as a linear combination of a set of two-dimensional linear basis functions smoothed with bi-cubic spline interpolation. The coefficients of the basis functions in the linear combination are obtained from the local extrema of the data using a generalized low-pass filter. By taking advantage of the principle of finite-element analysis, we develop a fast algorithm for implementation of the EMD. The proposed method provides an effective approach to overcome several challenging difficulties in extending the original one-dimensional EMD to the two-dimensional EMD. Numerical experiments using both simulated and practical texture images show that the proposed method works well.

An Improved Empirical Mode Decomposition Based on Time Scale Allocation Method and the 2D Mode Mixing Phenomenon Judgement

2017 ◽  
Vol 09 (02) ◽  
pp. 1750002 ◽  
Author(s):  
Shu-Mei Guo ◽  
Jason Sheng-Hong Tsai ◽  
Chin-Yu Chen ◽  
Tzu-Cheng Yang
Keyword(s):  
Empirical Mode Decomposition ◽  
Time Scale ◽  
Computation Time ◽  
Image Data ◽  
Two Dimensional ◽  
Mode Function ◽  
Allocation Method ◽  
Mode Decomposition ◽  
Great Performance ◽  
Mode Mixing

In the sifting process of the traditional empirical mode decomposition (EMD), intermittence causes mode mixing phenomenon. The intrinsic mode function (IMF) with the mode mixing phenomenon loses its original real physical meaning. An improved EMD based on time scale allocation method and the two-dimensional (2D) version of our method has been extended to improve the decomposition of the mode mixing phenomenon in 2D image data. Experimental results show that the method not only improves the phenomenon correctly both for 1D signal and 2D image, but also exhibits great performance in quality and computation time.

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