Image dividing method based on immune multi-object clustering

An image segmentation and multi-objective technology, applied in the field of image processing, to achieve the effect of good diversity, good segmentation results, and fast calculation speed

Inactive Publication Date: 2010-10-20
XIDIAN UNIV
View PDF0 Cites 19 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The artificial immune system has the ability of self-adaptation, self-organization and self-learning, and can solve many complex problems that are difficult to solve by traditional methods

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 dividing method based on immune multi-object clustering
  • Image dividing method based on immune multi-object clustering
  • Image dividing method based on immune multi-object clustering

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0037] refer to figure 1 with figure 2 , the specific implementation steps of the present invention are as follows:

[0038]Step 1. Extract the features of the image and use the watershed method for initial segmentation. First, perform three-layer wavelet decomposition on the image, extract the feature quantities of 10 sub-generations, and obtain the 10-dimensional wavelet feature of each pixel; secondly, select according to the gray level co-occurrence matrix. Four directions, respectively 0°, 45°, 90° and 135°, extract three quadratic statistics along each direction as texture feature quantities, and the three statistics are homogeneous area, angular second-order moment and Correlation, a total of 12-dimensional feature information; plus the previously obtained 10-dimensional wavelet features to form a total of 22-dimensional features; in order to reduce the amount of calculation, the watershed method is used for initial segmentation, and the number of blocks that are much...

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 dividing method based on immune multi-object clustering, relating to the technical field of image processing, and mainly solving the problems that the conventional method has single evaluation index, and easily has bad region consistency and disorder boundary. The method comprises the following realization steps of: (1) extracting the characteristic of an image to be divided, and primarily dividing the image by controlling the watershed of a mark; (2) setting a running parameter and initializing the population of an antibody; (3) combining the locally-searched immune multi-objective optimizing method with the population of the antibody to obtain an approximate Pareto solution set; (4) selecting an optimal solution in the approximate Pareto solution set obtained in the step (3) according to a PBM index; and (5) marking an image pixel point according to a primary dividing result obtained in the step (1) and a clustering result obtained in the step (4) to obtain a final classifying result. The image dividing method has the advantages of good dividing result region consistency, being capable of keeping complete information, and having fast computation speed, and can be used for identifying an image object.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to an image segmentation method, and can be applied to 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. Image segmentation is to divide an image into multiple regions or objects, which is a basic technique in image processing. 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. In the feature-based image segmentation method, each pixel is represented by its image features. As long as the labels of these features are found, the classification of pixels can be realized, so as to achiev...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/12
Inventor 马文萍焦李成张娟王爽钟桦李阳阳朱虎明于昕尚荣华
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap