Image analysis by bidimensional empirical mode decomposition

Abstract : Recent developments in analysis methods on the non-linear and non-stationary data have received large attention by the image analysts. In 1998, Huang introduced the empirical mode decomposition (EMD) in signal processing. The EMD approach, fully unsupervised, proved reliable monodimensional (seismic and biomedical) signals. The main contribution of our approach is to apply the EMD to texture extraction and image filtering, which are widely recognized as a difficult and challenging computer vision problem. We developed an algorithm based on bidimensional empirical mode decomposition (BEMD) to extract features at multiple scales or spatial frequencies. These features, called intrinsic mode functions, are extracted by a sifting process. The bidimensional sifting process is realized using morphological operators to detect regional maxima and thanks to radial basis function for surface interpolation. The performance of the texture extraction algorithms, using BEMD method, is demonstrated in the experiment with both synthetic and natural images.
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Journal articles
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https://www.hal.inserm.fr/inserm-00177506
Contributor : Jean-Claude Nunes <>
Submitted on : Thursday, October 11, 2007 - 6:04:43 PM
Last modification on : Wednesday, May 16, 2018 - 11:23:08 AM

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Jean-Claude Nunes, Yasmina Bouaoune, Eric Delechelle, Oumar Niang, Philippe Bunel. Image analysis by bidimensional empirical mode decomposition. Image and Vision Computing, Elsevier, 2003, 21 (12), pp.1019-1026. ⟨10.1016/S0262-8856(03)00094-5⟩. ⟨inserm-00177506⟩

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