Texture analysis based on the bidimensional empirical mode decomposition with gray-level co-occurrence models

Abstract

We present a texture analysis algorithm based on gray-level cooccurrence (GLC) model and bidimensional empirical mode decomposition (BEMD) of a texture field. The EMD, which has been recently introduced in signal processing by Huang in 1998, is adaptive for nonlinear and nonstationary data analysis. The main contribution of our approach is to apply the empirical mode decomposition to texture extraction and image denoising. This decomposition, obtained by the bidimensional sifting process, plays an important role in the characterization of regions in textured images. The sifting process is realized using morphological operators to detect regional extrema and thanks to radial basis functions (RBF) for interpolation. We modified the original sifting process to permit a texture decomposition of images by inserting criteria proposed by second-order statistics from GLCs.

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