Texture Classification based on Bidimensional Empirical Mode Decomposition and Local Binary Pattern
نویسندگان
چکیده
منابع مشابه
Texture Classification based on Bidimensional Empirical Mode Decomposition and Local Binary Pattern
This paper presents a new simple and robust texture analysis feature based on Bidimensional Empirical Mode Decomposition (BEMD) and Local Binary Pattern (LBP). BEMD is a locally adaptive decomposition method and suitable for the analysis of nonlinear or nonstationary signals. Texture images are decomposed to several Bidimensional Intrinsic Mode Functions (BIMFs) by BEMD, which present a new set...
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This study introduces a new approach based on Bidimensional Empirical Mode Decomposition (BEMD) to extract texture features at multiple scales or spatial frequencies. Moreover, it can resolve the intrawave frequency modulation provided the frequency modulation. This decomposition, obtained by the bidimensional sifting process, plays an important role in the characterization of regions in textur...
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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 ex...
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ژورنال
عنوان ژورنال: International Journal of Advanced Computer Science and Applications
سال: 2013
ISSN: 2158-107X,2156-5570
DOI: 10.14569/ijacsa.2013.040933