JOURNAL ARTICLE

Image feature extraction and analysis based on Empirical mode decomposition

Abstract

This Empirical mode decomposition (EMD) is a kind of multi-scale transformation theory which is suitable for nonlinear and non-stationary signal processing. It is not necessary to select the basis function in advance, and can adaptively adjust according to the characteristics of the signal itself. Extracting the intrinsic mode function (IMF) is an important process for the applications for empirical mode decomposition in one or two-dimensional image processing. The choice of decomposition scale and the extraction and selection of the intrinsic mode function are the principal and basic content for the right application and understanding. Aiming at these problems, this paper has discussed and studied them in depth, and some actual images are used to verify the feature extraction method, and the corresponding conclusions are obtained from the experimental results.

Keywords:
Hilbert–Huang transform Feature extraction Decomposition Computer science Pattern recognition (psychology) Mode (computer interface) Artificial intelligence Process (computing) SIGNAL (programming language) Principal component analysis Function (biology) Nonlinear system Functional decomposition Signal processing Image processing Image (mathematics) Transformation (genetics) Feature (linguistics) Computer vision Machine learning Digital signal processing Physics

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FWCI (Field Weighted Citation Impact)
9
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0.21
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Citation History

Topics

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