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Digital image analysis method based on fractal dimension

A digital image and fractal dimension technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as long calculation time, difficult to fully reflect, and increased computational complexity.

Active Publication Date: 2012-11-28
HEFEI UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0010] ①Insufficient image analysis ability
With the increase of the color information of the image to be processed, the complexity of the distribution space of the color attribute value increases the difficulty of image analysis. The traditional fractal dimension calculation method mostly solves the dimension of the grayscale image, ignoring the real color image. The capture of a variety of color information in the image makes it difficult for the results obtained by traditional calculation methods to fully reflect the impact of different color attributes on the image analysis results
[0011] ②Long calculation time
When it is necessary to repeatedly compare each pixel, the complexity of the calculation method will be greatly increased, resulting in too long calculation time
In addition, although some improved fractal dimension calculation methods can obtain fractal dimensions that meet the characteristics of human vision, they need to decompose the image, resulting in a significant increase in calculation time

Method used

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  • Digital image analysis method based on fractal dimension
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  • Digital image analysis method based on fractal dimension

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0185] Example 1: Using this method to figure 2 Calculate the fractal dimension of the grayscale texture image in

[0186] According to the first step of this method, the color attribute information of each pixel point in the grayscale texture image (that is, the pixel point position, gray value or three primary color component values) is regarded as a set of vector attributes of the pixel point, through a m The ×n matrix U is saved.

[0187] According to the second step of the method, the image information conversion method is performed on the matrix U, and then the low-dimensional pixel point space Y that is homeomorphic to U is obtained. (Because the data in the position pixel space Y is too large, only five data are taken out to show its structure)

[0188] Y=[1.5761,1.5239,0.6454,…,-2.6334,-2.0134];

[0189] Calculate the corresponding fractal dimension according to the third step in this method:

[0190] D=2.0154;

[0191] The fitting error of this method is 0.0002...

example 2

[0194] Example 2: Using this method to image 3 Calculate the fractal dimension of the color image in, where image 3 The original image is a color image, because the patent application can only use black and white images, so the displayed image is a black and white image;

[0195] According to the first step of this method, the color attribute information of each pixel point in the grayscale texture image (that is, the pixel point position, gray value or three primary color component values) is regarded as a set of vector attributes of the pixel point, through a m The ×n matrix U is saved.

[0196] According to the second step of the method, the image information conversion method is performed on the matrix U, and then the low-dimensional pixel point space Y that is homeomorphic to U is obtained. (Because the data in the position pixel space Y is too large, only five data are taken out to show its structure)

[0197] Y=[1.1101,1.0069,1.1530,...,-1.6602,-0.9400];

[0198] ...

example 3

[0203] Example 3: Using this method to Figure 4 Incremental simulation test for part of the image data in ;

[0204] According to the first step of the method of the present invention, the color attribute information of each pixel point in the grayscale texture image (that is, the pixel point position, gray value or three primary color component values) is regarded as a set of vector attributes of the pixel point, through a The m×n matrix U is saved.

[0205] According to the second step of this method, the image information conversion method is performed on the matrix U, and then the low-dimensional pixel point space Y that is homeomorphic to U is obtained 1 .

[0206] Y 1 =[1.6309, -0.0771, -0.7982, 0.4624, -1.2180];

[0207] And according to the LLE method for Figure 4 Part of the image data in the calculation, the result Y 2 for:

[0208] Y 2 =[1.6309, -0.0771, -0.7982, 0.4624, -1.2180];

[0209] because Y 1 =Y 2 , so the experiment shows that the relationship...

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Abstract

The invention discloses a digital image analysis method based on fractal dimension. The method is characterized by comprising the following steps: extracting all color attribute information included in a digital image through an image information matrix U; calculating the distance between pixel points by use of the image information matrix U; performing increment judgment on elements in the image information matrix U; selecting an increment manifold algorithm or non-increment manifold algorithm according to the increment judgment result to obtain a low-dimensional pixel point space of the image information matrix U; calculating the distance between any two pixel points in the low-dimensional pixel point space by use of the low-dimensional pixel point space; and calculating the fractal dimension of the digital image by use of the obtained distance between the pixel points so as to realize classification of different digital images. The method disclosed by the invention can effectively obtain the fractal dimension from the image to be processed, ensures relatively least fitting error, and realizes good classification ability on the digital image.

Description

technical field [0001] The invention relates to an image analysis method, in particular to a digital image analysis method based on fractal dimension, which belongs to the field of information system and information management. Background technique [0002] Image analysis refers to the process of using mathematical models and image processing techniques to analyze the low-level features and upper-level structure of images to obtain image information with certain value. Since the 1960s, there have been many research results in image analysis, and the image analysis technology for specific problems and applications has gradually developed to the direction of establishing general theories. Image analysis is mainly divided into four processes: input, segmentation, recognition, and interpretation. Aiming at different processes of image analysis, researchers have proposed a series of image analysis methods, the main methods are: statistical geometric feature method, stochastic mo...

Claims

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Application Information

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IPC IPC(8): G06T7/60
Inventor 罗贺王洪波
Owner HEFEI UNIV OF TECH
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