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

Abstract

International audienceThe 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

Texture Classification based on Bidimensional Empirical Mode Decomposition and Local Binary Pattern

“…Nunes. [9] firstly extended it to two dimensional BEMD, and decomposed images to bidimensional IMFs (BIMFs). The Bidimensional EMD (BEMD) process is conceptually the same as the one dimension EMD.…”

Remote Sensing Image Fusion Method Based on improved Radial Basis and Fast partition Interpolation

“…Compared to traditional and integral transform methods, EMD can offer better time-frequency of decomposition for non-linear, non-stationary signal, its current research focused on solving regional extreme point, solving envelope by the plane interpolation, boundary processing, screening termination conditions, fast algorithms, etc [3]. A bidimensional empirical mode decomposition method is proposed [4], which is based on the principles of morphology and radial basis function (RBF) interpolation. It is possible to reconstruct a relatively smooth surface, but the RBF will along with a large amount of computation and slow calculation speed.…”

Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis

“…Accordingly, BEMD overcomes the wavelet transformation drawback and provides a promising image processing technique that can be applied in various areas of image analysis, e.g. image fusion [14], image compression [15][16], texture analysis [17][18], feature extraction [19], etc. Numerous expansions of BEMD are afterwards developed for image enhancement.…”