Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Image segmentation method based on genetic rough set C-mean clustering

A mean value clustering and image segmentation technology, which is applied in image enhancement, image data processing, instruments, etc., can solve the problems of weakening and recognizing inconspicuous objects in images, losing local information, etc., achieve accurate image segmentation results, improve robustness and Reliability, the effect of improving the ability of inconspicuous targets

Active Publication Date: 2012-04-25
探知图灵科技(西安)有限公司
View PDF4 Cites 20 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method has strong anti-noise ability, fast convergence speed, and can improve the stability of the image segmentation effect. However, the disadvantage of this method is that it only uses the neighborhood information of the image, and it loses too much due to over-smoothing in the case of complex images. Local information, which weakens the ability to identify inconspicuous objects in the image

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Image segmentation method based on genetic rough set C-mean clustering
  • Image segmentation method based on genetic rough set C-mean clustering
  • Image segmentation method based on genetic rough set C-mean clustering

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0050] Attached below figure 1 The steps of the present invention are further described.

[0051] Step 1, input an image to be segmented

[0052] Step 2, extract image texture features

[0053] First, the wavelet decomposition method is used to extract the first 10-dimensional features of all pixels of the image to be segmented;

[0054] The wavelet decomposition method uses three-level wavelet transform with a window size of 32×32 on the image to obtain wavelet features composed of subband coefficients, which are used as the first 10-dimensional wavelet feature vector of each pixel.

[0055] Then, use the gray level co-occurrence matrix method to extract the last 12-dimensional features of all pixels of the image to be segmented;

[0056] The steps of the gray level co-occurrence matrix method are as follows:

[0057] Vectorize the image into L=16 gray levels, and then sequentially set the angles between the two pixel points and the horizontal axis to be 0°, 45°, 90° and ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses an image segmentation method based on genetic rough set C-mean clustering, which mainly solves the problem that the conventional method has poor robustness, easily falls into local optimum and loses too much local information. The method comprises the implementation steps of: (1) inputting a to-be-segmented image; (2) extracting image texture features; (3) generating clustering object data; (4) initializing population; (5) updating membership; (6) dividing the clustering object data; (7) updating the population; (8) calculating an individual fitness value; (9) evolving the population; (10) judging whether a termination condition is satisfied; (11) generating an optimal individual; (12) marking; (13) generating segmented images. In the method, the texture features of each pixel of the image are extracted, and the texture features are marked through the C-mean clustering method based on the genetic algorithm and the thought of rough set so as to divide the pixels, thus, stability of image segmentation is improved, and more accurate image segmentation result is obtained.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an improved generalized fuzzy c-means clustering algorithm based on GA and rough set in the technical field of image segmentation. The invention can be used to segment synthetic aperture radar SAR images and natural images to achieve the purpose of target recognition. Background technique [0002] Applying intelligent computing technology to image segmentation is a popular research direction in the field of image segmentation in recent years, mainly including neural network, genetic algorithm, swarm intelligence algorithm and artificial immune system framework. From the perspective of segmentation results, the process of image segmentation is to assign a label to each pixel, which reflects the category of the pixel in the segmentation result. As long as the labels of these features are found, the classification of pixels can be realized, and the result of image se...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06T5/00
Inventor 马文萍焦李成葛小华公茂果马晶晶
Owner 探知图灵科技(西安)有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com