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
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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 segmentat

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  • 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

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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; ...

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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...

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Application Information

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IPC IPC(8): G06T7/11G06T5/00
CPCG06T7/11G06T5/002G06T2207/20112G06T2207/10004
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
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