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==Motivation==
Multidimensional empirical mode decomposition is a popular method because of its applications in many fields, such as texture analysis, financial applications, [[Digital image processing|image processing]], [[Ocean Engineering|ocean engineering]], [[Seismology|seismic]] research, etc. Recently, several methods of Empirical Mode Decomposition have been used to analyze characterization of multidimensional signals. In this article, we will introduce the basics of Multidimensional Empirical Mode Decomposition, and then look into various approaches used for Multidimensional Empirical Mode Decomposition.
==Introduction to empirical mode decomposition (EMD)==
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