Machine Vision and Applications

, Volume 16, Issue 3, pp 177–188

Texture analysis based on local analysis of the Bidimensional Empirical Mode Decomposition


DOI: 10.1007/s00138-004-0170-5

Cite this article as:
Nunes, J.C., Guyot, S. & Deléchelle, E. Machine Vision and Applications (2005) 16: 177. doi:10.1007/s00138-004-0170-5


The main contribution of our approach is to apply the Hilbert-Huang Transform (which consists of two parts: (a) Empirical Mode Decomposition (EMD), and (b) the Hilbert spectral analysis) to texture analysis. The EMD is locally adaptive and suitable for analysis of non-linear or non-stationary processes. This one-dimensional decomposition technique extracts a finite number of oscillatory components or “well-behaved” AM-FM functions, called Intrinsic Mode Function (IMF), directly from the data. Firstly, we extend the EMD to 2D-data (i.e. images), the so called bidimensional EMD (BEMD), the process being called 2D-sifting process. The 2D-sifting process is performed in two steps: extrema detection by neighboring window or morphological operators and surface interpolation by radial basis functions or multigrid B-splines. Secondly, we analyse each 2D-IMF obtained by BEMD by studying local properties (amplitude, phase, isotropy and orientation) extracted from the monogenic signal of each one of them. The monogenic signal is a 2D-generalization of the analytic signal, where the Riesz Transform replaces the Hilbert Transform. The performance of this texture analysis method, using the BEMD and Riesz Transform, is demonstrated with both synthetic and natural images.


Texture analysis Empirical mode decomposition Surface interpolation Mathematical morphology Hilbert-Huang transform Riesz transform Monogenic signal Structure multi-vector 

Copyright information

© Springer-Verlag Berlin/Heidelberg 2005

Authors and Affiliations

  1. 1.Laboratoire d’Etude et de Recherche en Instrumentation, Signaux et Systémes (LERISS, E.A. 412)Université Paris XII, Val de MarneCréteil CedexFrance

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