Texture Classification Through Directional Empirical Mode Decomposition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
A novel boundary extension approach for empirical mode decomposition
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
A flexible method for envelope estimation in empirical mode decomposition
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Texture analysis based on saddle points-based BEMD and LBP
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
The complex bidimensional empirical mode decomposition
Signal Processing
An improved images watermarking scheme using FABEMD decomposition and DCT
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
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A novel approach for bidimensional empirical mode decomposition (BEMD) is proposed in this paper. BEMD decomposes an image into multiple hierarchical components known as bidimensional intrinsic mode functions (BIMFs). In each iteration of the process, two-dimensional (2D) interpolation is applied to a set of local maxima (minima) points to form the upper (lower) envelope. But, 2D scattered data interpolation methods cause huge computation time and other artifacts in the decomposition. This paper suggests a simple, but effective, method of envelope estimation that replaces the surface interpolation. In this method, order statistics filters are used to get the upper and lower envelopes, where filter size is derived from the data. Based on the properties of the proposed approach, it is considered as fast and adaptive BEMD (FABEMD). Simulation results demonstrate that FABEMD is not only faster and adaptive, but also outperforms the original BEMD in terms of the quality of the BIMFs.