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

Image segmentation method of high-order MRF model based on multi-node topology overlapping measure

An image segmentation and multi-node technology, applied in the field of image processing, can solve the problem that the image segmentation model cannot effectively describe the high-order topological structure characteristics of complex images, etc., to improve the expression ability of prior knowledge, enhance the expression ability, and be robust effect with effectiveness

Active Publication Date: 2020-04-14
XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
View PDF4 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide an image segmentation method based on a high-order MRF model based on a multi-node topological overlapping measure to solve the image segmentation of a high-order MRF with conventional constraint region consistency. The model cannot effectively describe the high-order topological features of complex images, and can effectively improve the effect of image segmentation

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 of high-order MRF model based on multi-node topology overlapping measure
  • Image segmentation method of high-order MRF model based on multi-node topology overlapping measure
  • Image segmentation method of high-order MRF model based on multi-node topology overlapping measure

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0054] see figure 1 , the present invention a kind of image segmentation method based on the high-order MRF (MTOM-HMRF) model of multi-node topological overlapping measure, comprises the following steps:

[0055] S1. Input a natural image to be segmented X={x s |x s ∈Ω,s∈S}, where Ω={0,1,…,255} represents the observed pixel x in the image s The range of intensity values, S represents a finite set of grid points; define the label field Y={y of the segmented image s |y s ∈Λ, s∈S}, Λ={0, 1, ..., L}, L represents the total number of image segmentation labels;

[0056] S2, parameter initialization;

[0057] Given a local region w s , the number of classification labels L; the mean and variance of WGMM Random initialization; prior parameter β; normalization parameter ρ, power adjacency parameter γ; Gibbs sampling algorithm initial temperature T (0) ;

[0058] Among them, the number of segmentation categories L is manually determined according to the image to be segmented; ...

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 of a high-order MRF model based on multi-node topology overlapping measure. The method comprises the steps of firstly, inputting a to-be-segmentednatural image; then initializing parameters; constructing a high-order MRF priori energy item based on MTOM; establishing a high-order MRF image segmentation energy model based on the multi-node topological overlapping measure according to the local region consistency WGMM likelihood energy and a region-based partial second-order Potts prior model; and optimizing the high-order MRF image segmentation energy model by adopting a Gibbs sampling algorithm, and determining an image segmentation result. According to the method, strong noise and texture mutation interference of the image can be effectively resisted, the robustness is better, and more accurate image segmentation edges are provided.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image segmentation method based on a high-order MRF model of multi-node topological overlapping measure. Background technique [0002] Image segmentation is one of the core issues in the field of computer vision research, and it is the basis for higher-level analysis and understanding of images. In recent years, the image segmentation method based on the Markov random field (MRF) model has received extensive attention and has become a research hotspot in the field of image segmentation. Under the framework of probability, MRF uses the Gibbs distribution of image pixel labels to describe the prior knowledge of image local space, and based on Bayesian theorem, combines the prior knowledge of image space with likelihood features, and has achieved success in the field of image segmentation. Applications. [0003] Since the low-order MRF model can only expres...

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): G06T7/11G06T5/00
CPCG06T7/11G06T2207/20112G06T2207/10004G06T5/70
Inventor 徐胜军周盈希孟月波刘光辉史亚孔月萍
Owner XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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