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Gray level image segmentation method based on multi-objective fuzzy clustering

A grayscale image, fuzzy clustering technology, applied in the field of image processing, can solve problems such as poor regional consistency, local optimum, initial value and noise sensitivity

Active Publication Date: 2013-12-25
陕西国博政通信息科技有限公司
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

However, the shortcomings of the FCM algorithm when processing image segmentation are: (1) The FCM algorithm does not fully consider the spatial information of the image, and only clusters all samples as scattered sample points, resulting in the final segmentation results being inconsistent in the region Very poor, there are noise points inside the region; (2) The FCM algorithm is sensitive to the initial value and noise, and it is easy to fall into the local optimum, resulting in poor segmentation effect

Method used

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Experimental program
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Embodiment 1

[0057] Image segmentation is an important link in the image processing process, which provides the information basis for subsequent target recognition, feature extraction and other work in image processing. The minimum hardware configuration required to realize the image segmentation process is: personal computer processor 1GHz, memory 1GB, software environment: software such as matlab or VC.

[0058] refer to figure 1 , the present invention is a kind of grayscale image segmentation method based on multi-target fuzzy clustering, and the realization process comprises the following steps:

[0059] Step 1, read in a noise-free gray-scale image I, the gray-scale image can be obtained through the gray-scale processing of the digital image, and the gray-scale image is usually saved with a nonlinear scale of 8 bits per sampling pixel, so that There can be 256 gray levels.

[0060] In this embodiment, read in a grayscale image House, see figure 2 (a) is the grayscale image I to b...

Embodiment 2

[0110] The grayscale image segmentation method based on multi-objective fuzzy clustering is the same as that in Example 1. In Example 2, the comparison experiment is a classic fuzzy C-means image segmentation method, and the image segmentation results are compared with grayscale images. In Example 2, the grayscale image House is used as the input image, the image size is 227×227 pixels, the grayscale is 256, and the maximum number of iterations is g max Take 50, the number of image segmentation categories K is set to 3, that is, the grayscale image is divided into three. Adopt the present invention to process final classification result figure see figure 2 (e).

[0111] figure 2 (a) is the original grayscale image of House, figure 2 (c) is the three-category segmentation result map obtained by using the fuzzy C-means method of the comparative experiment, figure 2 (e) is the three-category segmentation result figure that adopts the method of the present invention to obt...

Embodiment 3

[0113] The grayscale image segmentation method based on multi-objective fuzzy clustering is the same as that in Example 1. In Example 3, the comparison experiment is a classic fuzzy C-means image segmentation method, and the image segmentation results are compared with grayscale images. In Example 3, the grayscale image lena is used as the input image, the image size is 256×256 pixels, the grayscale is 256, and the maximum number of iterations is g max Take 50, and set the number of image segmentation categories K to 2, that is, to divide the gray image into two. For the final classification results, see image 3 (d).

[0114] image 3 (a) is the original grayscale image of lena, image 3 (b) is the two-category segmentation result map obtained by using the fuzzy C-means method of the comparative experiment, image 3 (d) is a two-category segmentation result map obtained by the method of the present invention, from image 3 (b) and image 3 (d) Contrast can find out: the...

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Abstract

The invention discloses a gray level image segmentation method based on multi-objective fuzzy clustering, relating to the technical field of image processing and mainly solving the problem of lower accuracy rate of gray level image segmentation. The gray level image segmentation method comprises the steps of: after graying an image, randomly generating a plurality of clustering centers according to a generated grey level histogram, and constituting the clustering centers into a parent antibody population. The gray level image segmentation method is characterized in that a dense separation effectiveness function as an evaluation criteria is combined with a fuzzy optimization function in a fuzzy C-mean value method to form a multi-objective optimization problem, the whole parent population is iterated for multiple times by adopting an immune clone multi-objective evolutionary algorithm, simultaneously searched from multiple directions, and calculated in parallel so as to finally acquire an optimum clustering center, and a classifying result is output. Therefore, the detail information in the gray level image is effectively reserved, the wrong fraction is reduced, the gray level image segmentation precision is improved, and a good platform is provided for subsequent operation of gray level image segmentation. The gray level image segmentation method can be used for extracting and obtaining the detail information of the gray level image.

Description

technical field [0001] The invention belongs to the field of image processing, and mainly relates to a grayscale image segmentation method, in particular to a grayscale image segmentation method based on multi-target fuzzy clustering, which can be used to extract detailed information of grayscale images, and is used for subsequent target recognition, Work such as feature extraction provides a better information basis. Background technique [0002] With the development of various imaging technologies, people's demands and applications for image processing are increasing day by day. For example: network images, remote sensing images, synthetic aperture radar images, etc. have become important research fields. Image segmentation is an important issue in image processing. It is not only a test of the effect of all image preprocessing, but also the basis for subsequent image analysis and interpretation. Image segmentation is to separate different areas with special meanings in ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
Inventor 尚荣华焦李成王佳马文萍公茂果齐丽萍李阳阳王爽马晶晶
Owner 陕西国博政通信息科技有限公司
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