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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Texture Classification Based on BIMF Monogenic Signals
verfasst von : JianJia Pan, Yuan Yan Tang
Erschienen in: Computer Vision – ACCV 2012
Verlag: Springer Berlin Heidelberg
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This paper proposes a new texture feature based on HHT, Riesz transform and LBP. Hilbert-Huang transform (HHT) is a novel efficient signal analysis method proposed by N.E.Huang. It consists two parts: Empirical Mode Decomposition (EMD) and Hilbert transform. Images are decomposed to several Bidimensional Intrinsic Mode Functions (BIMFs) by BEMD, which present new multi-scale characters and present illumination invariant. And then, for two-dimensional signal BIMFs, we proposed using the Riesz transform instead of Hilbert transform to generate monogenic signals, which are rotation invariant. After then, Local Binary Pattern (LBP) detected the features from the Monogenic-BIMFs space. Experiments demonstrate the LBP histogram of Monogenic-BIMFs present a better classification result than other state-of-the-art texture representation methods.