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Image rotation-based calculation method for any gray-level co-occurrence matrix and application of method

A technology of gray-scale co-occurrence matrix and image rotation, which is applied in graphics and image conversion, calculation, image analysis and other directions to achieve the effect of expanding application space, application effect, and application convenience.

Active Publication Date: 2017-05-31
SECOND INST OF OCEANOGRAPHY MNR
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the traditional GLCM image texture analysis is limited by the position of the image pixels, and generally only considers the GLCM in a specific direction (usually the horizontal direction, vertical direction and diagonal direction of the image); therefore, it is necessary to invent a method that can calculate the relative Calculation method of GLCM for position

Method used

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  • Image rotation-based calculation method for any gray-level co-occurrence matrix and application of method
  • Image rotation-based calculation method for any gray-level co-occurrence matrix and application of method
  • Image rotation-based calculation method for any gray-level co-occurrence matrix and application of method

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Experimental program
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Embodiment 2

[0049] The GLCM corresponding to any relative position obtained by the calculation method in Embodiment 1 of the present invention is added with the transposition of the matrix, and then all matrix elements of the matrix sum are divided by 2 to obtain a symmetrical GLCM.

Embodiment 3

[0051] The invention can calculate the GLCM of an image with respect to any relative position, makes the application of the GLCM in various fields more convenient, and expands the application space and application effect of the GLCM. Hereinafter, the application of the present invention in the field of fine texture direction estimation is taken as an example for illustration.

[0052] A method for fine estimation of texture direction based on GLCM, adopting the method provided by the present invention to calculate the required GLCM, such as Figure 8 shown, including the following steps:

[0053] Step 1: Select an image, and divide the direction and distance of the relative position into equal intervals. The direction is represented by an angle θ, and the angle division range is -90° to 90°, where "angle" is the direction in the relative position and the horizontal direction of the image The angle between, "distance" is the distance between the two ends of the relative positi...

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Abstract

The invention discloses an image rotation-based calculation method for any gray-level co-occurrence matrix (GLCM) and an application of the method. The GLCM, about any relative direction and distance, of an image is calculated through image rotation, so that the GLCM calculation is not limited by specific angles, distances and the like. Therefore, the application of the GLCM to various fields becomes more convenient, and the application space and the application effect of the GLCM are expanded and improved.

Description

technical field [0001] The present invention relates to a calculation method of GLCM (Gray-level co-occurrence matrix, Gray-level co-occurrence matrix), in particular to a calculation method and application of an arbitrary gray-level co-occurrence matrix based on image rotation. Background technique [0002] GLCM is a common analysis tool for describing texture through the spatially correlated properties of grayscale. Texture can be recognized naturally by human vision from many visible surfaces, and it can give people some special feelings, such as sense of direction, period and roughness. As an intrinsic property of visible surfaces, texture is a very important research topic in many fields. Many research works around texture are exploring how to better extract the characteristics of texture to describe it objectively, and GLCM is one of the developed statistical analysis tools. [0003] The traditional GLCM is essentially a joint probability distribution of gray values ...

Claims

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Application Information

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IPC IPC(8): G06T7/45G06T3/60
CPCG06T3/60
Inventor 郑罡
Owner SECOND INST OF OCEANOGRAPHY MNR
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