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A Method of Texture Feature Extraction in Gray Image Based on Orientation Selectivity

A texture feature and extraction method technology, applied in the field of image processing, can solve the problems of poor texture feature effect, noise sensitivity, and inapplicability to noise image classification problems.

Active Publication Date: 2018-03-06
XIDIAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, LBP has a big defect that limits its application, that is, it is sensitive to noise, and the extracted texture features are less effective in classifying noisy images, and are not suitable for the classification of noisy images.

Method used

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  • A Method of Texture Feature Extraction in Gray Image Based on Orientation Selectivity
  • A Method of Texture Feature Extraction in Gray Image Based on Orientation Selectivity
  • A Method of Texture Feature Extraction in Gray Image Based on Orientation Selectivity

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0060] Embodiment 1: the weighted texture feature extraction based on direction selectivity

[0061] The implementation steps of this example are as follows:

[0062] Step 1. Simulate the spatial structure distribution characteristics of any pixel point x in the image according to the orientation selection principle of the optic nerve, and obtain the spatial structure distribution calculation formula.

[0063] (1a) The input image to be processed is N×N Take any pixel point x from it, and simulate the pixel point according to the orientation selection principle of the optic nerve The spatial structure distribution characteristics of

[0064]

[0065] in Indicates the spatial structure distribution characteristics of the pixel point x, represents an arrangement of responses enclosed in parentheses, Represents the pixel point x and the surrounding circular area the interaction between them. is a collection of n pixels selected from the circular area around t...

Embodiment 2

[0092] Example 2: Extraction of texture features based on direction selectivity

[0093] The implementation steps of this example are as follows:

[0094] Step 1 is the same as Step 1 of Embodiment 1

[0095] Step 2 is the same as Step 2 of Example 1

[0096] Step 3 is the same as Step 3 of Example 1

[0097] Step 4 is the same as Step 4 of Example 1

[0098] Step five, directly count the spatial structure mode of the pixel point x The number of texture histograms drawn.

[0099] (5.1) Direct statistical image All conforming to the k-th direction-selective mode in n categories The number of spatial structure distributions H(k):

[0100]

[0101] in N represents the size of the input image, k∈(1~n);

[0102] (5.2) Use the MATLAB tool to draw the percentage of H(k) in the total number of spatial structure distributions into a texture histogram, which is the result of texture feature extraction from the image.

[0103] Effect of the present invention can be furth...

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Abstract

The invention discloses a textural feature extracting method based on orientation selectivity. The problem that according to the prior art, when an LBP carries out textural classifying on images with noise, effect is bad is mainly solved. The method comprises the steps that 1. according to the optic nerve orientation selecting principle, the space structure distribution of image pixel points is simulated; 2. by comparing of an azimuth angle difference value between the pixel points and a set threshold value, the space structure distribution of pixel points is determined; 3. the space structure distribution of all the pixel points is reduced to several modes based on direction selectivity; 4. the gray level changing value of each pixel point is computed; and 5. the space structure distribution number of a certain mode in images is subjected to statistics, a textural column diagram is drawn, combining with the gray level changing values is carried out, and weighting textural column diagram is drawn. By simulating the selecting sensitivity on the orientation from the human optic nerve, interference on image texture classifying from noise is lowered, and the method can be used for image processing and computer vision related to image classifying, image understanding and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for extracting texture features of grayscale images which can be used for image classification. technical background [0002] With the rapid development of network technology and multimedia technology, a large number of different types of image data are emerging on the Internet. And image data has some characteristics that conventional data do not have, such as: non-uniform format, rich and diverse information content, and two-dimensionality of time and space. Therefore, how to complete image classification well has become a hot research topic at present. [0003] Texture feature is an important feature of images, because texture exists widely in nature, and it is an inherent characteristic of all object surfaces. Texture, which does not depend on brightness or color changes, is a visual feature that can represent homogeneous phenomena in images, a...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06K9/46
Inventor 吴金建万文菲张亚中石光明
Owner XIDIAN UNIV
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