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

Hierarchical fuzzy C-means based image segmenting method

An image segmentation and averaging technology, applied in the field of image processing, can solve the problems of high computational complexity, image noise influence, long calculation time, etc., and achieve the effect of good robustness, good noise resistance, and good image segmentation quality

Active Publication Date: 2014-08-06
广州市元博信息科技有限公司
View PDF2 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention proposes an image segmentation method based on layered fuzzy c-means in order to solve the problems that existing algorithms have disadvantages such as high computational complexity, long computational time, and still being easily affected by image noise.

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
  • Hierarchical fuzzy C-means based image segmenting method
  • Hierarchical fuzzy C-means based image segmenting method
  • Hierarchical fuzzy C-means based image segmenting method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0031] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0032] The existing fuzzy c-means image segmentation method mainly adopts the following method, let y i Represents the pixel value of the i-th point in the image, where i=(1,2,...,N), N is the total number of pixels in the image. j(j=1,2,...,K) represents the class corresponding to pixel i. Then, the objective function of fuzzy c-means (FCM) can be expressed as:

[0033] J m = Σ i = 1 N Σ j = 1 J u ij m d ij - - - ( 1 )

[0034] Among them, u ij is th...

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 a hierarchical fuzzy C-means based image segmenting method. The hierarchical fuzzy C-means based image segmenting method includes simultaneously applying mean templates to the membership degree and the distance function so as to acquire better image segmenting results, effectively utilizing spatial context messages in images and acquiring better image segmenting quality. Meanwhile, operation time and calculation amount of the algorithm are lower, and the distance function is a hierarchical fuzzy C model, that is, the distance function is considered as a sub-fuzzy C-mean model, so that the distance function has better noise immunity than the conventional Euclidean distance function. The algorithm of the hierarchical fuzzy C-means based image segmenting method has better robustness.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to an image segmentation method based on layered fuzzy c-means. Background technique [0002] Image segmentation is an important research topic in image processing, which determines the final results and quality of image analysis and image understanding. Because of the importance of image segmentation, many scholars at home and abroad have carried out a lot of research on it, and proposed various segmentation algorithms, but most of these algorithms are aimed at specific research objects, and there is no general segmentation theory so far. Therefore, people are still exploring new segmentation algorithms and segmentation theories. [0003] Fuzzy C-means algorithm is the most perfect theory and the most widely used algorithm in the clustering algorithm based on the objective function. Due to successfully introducing the fuzzy concept into the membership degree of image pixels, the f...

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): G06T7/00
Inventor 张辉陈北京郑钰辉
Owner 广州市元博信息科技有限公司
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