Joint spatial/spatial-frequency representation
Signal Processing
Texture Segmentation Using Voronoi Polygons
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Segmentation Using Fractal Dimension
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solution of an inverse heat transfer problem by means of empirical reduction of modes
Zeitschrift für Angewandte Mathematik und Physik (ZAMP)
Texture Classification Through Directional Empirical Mode Decomposition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Signal Period Analysis Based on Hilbert-Huang Transform and Its Application to Texture Analysis
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Neighborhood Limited Empirical Mode Decomposition and Application in Image Processing
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Parameter estimation of 2-D random amplitude polynomial-phasesignals
IEEE Transactions on Signal Processing
Hypercomplex signals-a novel extension of the analytic signal tothe multidimensional case
IEEE Transactions on Signal Processing
An estimation algorithm for 2-D polynomial phase signals
IEEE Transactions on Image Processing
Segmentation of textured images using a multiresolution Gaussian autoregressive model
IEEE Transactions on Image Processing
The complex bidimensional empirical mode decomposition
Signal Processing
On analysis of bi-dimensional component decomposition via BEMD
Pattern Recognition
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An improved bi-dimensional empirical mode decomposition (IBEMD) is proposed. Structure of image extremas represents the important feature of images, and is useful for the information extraction and analysis. The image extrema are classified into the five different sets, which are called as the structural extrema. The structural extrema are used instead of the classical extrema, and the BEMD (bi-dimensional empirical mode decomposition) algorithms based on the structural extrema are more accurate through interpolating the up and down envelopes. Specially, the IBEMD has the least NMSE (normalised mean square error) and the biggest SNR (signal-to-noise ratio) for the mode decomposition, and greatly improves the robustness of the BEMD. Moreover, quaternion Hilbert transform based space-spatial-frequency tool is improved, and applied to the texture analysis. The experiments of texture analysis show that the new approach is efficient for the application in texture analysis.