Method for extracting image characteristics by multivariate gray model-based bi-dimensional empirical mode decomposition

An empirical mode decomposition and gray model technology, applied in the field of image processing, can solve problems such as the inability to effectively extract image intrinsic information, and achieve the effects of high short-term prediction accuracy, suppression of end-point effects, and less data volume.

Active Publication Date: 2012-07-04
HARBIN INST OF TECH
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the existing two-dimensional empirical mode decomposition (Bi-dimensional Empirical Mode Decomposition, BEMD) method is affected by the endpoint effect and cannot effectively extract the intrinsic information of the image, and provides a method based on a multivariate gray model Two-dimensional Empirical Mode Decomposition Method for Extracting Image Features

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  • Method for extracting image characteristics by multivariate gray model-based bi-dimensional empirical mode decomposition
  • Method for extracting image characteristics by multivariate gray model-based bi-dimensional empirical mode decomposition
  • Method for extracting image characteristics by multivariate gray model-based bi-dimensional empirical mode decomposition

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specific Embodiment approach 1

[0033] Specific implementation mode one: the following combination figure 1 To describe this embodiment,

[0034] This embodiment is realized through the following technical solutions: For a given image IM original (x, y), let its original size and extended size be P×P and (P+2Q)×(P+2Q) respectively. The continuation area of ​​, respectively, is A, B, C and D four blocks. from IM original Select the best matching block B of T×T (T0, using a multivariate gray model MGM(1,3) (where "M" stands for multivariate, "G" stands for gray, "M" stands for model, "1" stands for model order 1, and "3" stands for model variables number is 3) predict B 0 The internal pixel values ​​are filled into the corresponding positions of the four blocks A, B, C, and D, and the small blocks are stitched by the minimum error path.

[0035] A method for extracting image features using a two-dimensional empirical mode decomposition based on a multivariate gray model described in this embodiment, the m...

specific Embodiment approach 2

[0049] Specific implementation mode 2: This implementation mode further explains the implementation mode 1, and the error tolerance ε in step 3 1 = 0.1.

specific Embodiment approach 3

[0050] Specific implementation mode three: this implementation mode further explains implementation mode one, and the error threshold ε in step six 2 = 0.2.

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Abstract

The invention discloses a method for extracting image characteristics by multivariate gray model-based bi-dimensional empirical mode decomposition, which belongs to the field of image processing and aims to solve the problem that image intrinsic information cannot be extracted effectively by the conventional bi-dimensional empirical mode decomposition method due to the influence of an end point effect. The method comprises the following steps of: performing boundary extension on a given image by using a multivariate gray model; performing the conventional bi-dimensional empirical mode decomposition on the extended image to acquire an extended bi-dimensional intrinsic mode function and a residual error; and extracting the bi-dimensional intrinsic mode function and the residual error at the position corresponding to the original image to be used as the final decomposition result. By the method, the advantages that a gray theory is low in data requirement quantity, high in short-term forecast accuracy and high in calculation speed and does not have particular requirements on the original data distribution are fully exerted, and the method is easy to implement, popularize and apply.

Description

technical field [0001] The invention relates to a method for extracting image features by using two-dimensional empirical mode decomposition based on a multivariate gray model, belonging to the field of image processing. Background technique [0002] In recent years, nonlinear and non-stationary data analysis methods have received extensive attention in the field of signal processing. Empirical Mode Decomposition (EMD), proposed by Norton Huang et al. in 1998, has become another new method after Fourier transform, wavelet transform, Wigner-Ville transform and other methods due to its powerful signal processing ability. research hotspots. In 2003, Nunes et al. extended EMD to two-dimensional situations, and proposed a two-dimensional empirical mode decomposition (Bi-dimensional Empirical Mode Decomposition, BEMD), which decomposes a given image into a limited number of two-dimensional local frequencies from high to low. The sum of Bi-dimensional Intrinsic Mode Function (BIM...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06T7/00
Inventor 沈毅贺智金晶林玉荣
Owner HARBIN INST OF TECH
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