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Texture Classification Method Based on Fractional Fourier Transform

A fractional Fourier, texture classification technology, applied in instruments, character and pattern recognition, computer parts and other directions, can solve problems such as low time-frequency resolution, inability to achieve correct segmentation, etc., to avoid cross-term problems. Effect

Active Publication Date: 2017-02-08
HARBIN ENG UNIV
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Problems solved by technology

On the other hand, if the time-frequency transform contains non-linear terms, the method will inevitably be affected by cross terms, which are sometimes so severe that correct segmentation cannot be achieved
Although linear transformations such as Gabor are not affected by cross terms, the time-frequency resolution of these linear methods is generally not high

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  • Texture Classification Method Based on Fractional Fourier Transform
  • Texture Classification Method Based on Fractional Fourier Transform
  • Texture Classification Method Based on Fractional Fourier Transform

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Embodiment Construction

[0026] The present invention is described in more detail below by way of example:

[0027] Step 1: Obtain an image I, the image I is a grayscale texture image of size N×N, (x, y) is a position coordinate in the texture image, where x=1,2,...,N ;y=1,2,…,N, I(x,y) represents the gray value of the texture image I at (x,y), d=0,1,2,3 respectively represent 0 shown in Figure 1 degree direction, 45 degree direction, 90 degree direction and 135 degree direction, I d (x, y, m) represents the gray value of the m-th neighborhood pixel of the pixel (x, y) in the d direction, where m=1, 2, 3, 4, 5;

[0028] Calculate the one-dimensional discrete FrFT of each pixel of the image in four directions:

[0029] First, for the point (x, y) in the image I, the neighborhood sequence of (x, y) in the directions of 0, 45, 90 and 135 degrees is obtained according to Figure 1. The obtained neighborhood pixel sequence is represented by { I d (x,y,m)|d=0,1,2,3,m=1,2,3,4,5} means;

[0030] Second, pu...

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Abstract

The invention provides a texture classification method on the basis of fractional Fourier transform (FrFT). The texture classification method comprises the following steps: (1) acquiring an image and calculating one-dimensional discrete FrFT of each pixel of the image in four directions; (2) carrying out descending sorting on each obtained one-dimensional discrete FrFT according to amplitudes; (3) calculating a fractional Fourier frequency histogram; and (4) carrying out classification on the texture image by utilizing a chi2- statistical distance classifier. The invention provides the texture classification method which is on the basis of FrFT and comprehensively utilizes the fractional Fourier frequency histogram and the chi2- statistical distance classifier. The texture classification method provided by the invention has the main effect of avoiding the problem of cross terms of Wigner distribution.

Description

technical field [0001] The invention relates to a classification method of texture images. Background technique [0002] Texture is a visual feature that does not depend on changes in color or brightness and reflects homogeneity in an image. Texture describes the distribution law of image pixel neighborhood gray space. In the real world, textures are ubiquitous, from the sky and grass in nature to the brick walls and cloth that are common in life, all of which have obvious texture characteristics. In the field of computer vision, texture classification is widely used in medical image processing, agricultural image segmentation, food quality supervision, and satellite image analysis as an important means of understanding real visual patterns. [0003] The key problem of texture classification is how to describe the texture. The existing texture description methods include: gray level co-occurrence matrix [1], bidirectional texture function [2], local binary pattern [3], af...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
Inventor 郑丽颖石大明田凯
Owner HARBIN ENG UNIV
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