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

Abstract : 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 ldquowell-behavedrdquo 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.
Complete list of metadatas

https://www.hal.inserm.fr/inserm-00177472
Contributor : Jean-Claude Nunes <>
Submitted on : Friday, October 12, 2007 - 4:15:12 PM
Last modification on : Wednesday, May 16, 2018 - 11:23:08 AM

Links full text

Identifiers

Citation

Jean-Claude Nunes, Steve Guyot, Eric Deléchelle. Texture analysis based on local analysis of the Bidimensional Empirical Mode Decomposition. Machine Vision and Applications, Springer Verlag, 2005, 16 (3), pp.0932-8092. ⟨10.1007/s00138-004-0170-5⟩. ⟨inserm-00177472⟩

Share

Metrics

Record views

336