Edge preserving multiresolution MRF (Markov Random Field) model image segmentation method

A technology of edge preservation and image segmentation, which is applied in image analysis, image enhancement, image data processing, etc., can solve the problems of blurred edges, low computational complexity, and lack of images in image segmentation

CN107909597AActive Publication Date: 2018-04-13XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2018-04-13

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Abstract

The invention discloses an edge preserving multiresolution MRF (Markov Random Field) model image segmentation method, which comprises the following steps that: firstly, on the basis of a multi-grid model, establishing a locally interacted image multiresolution grid segmentation model; then, utilizing a Cauchy model with an edge preserving function to extract the edge priori knowledge of the image;and establishing a local area interaction multiresolution MRF model which is combined with edge preserving to segment the image. Therefore, the fusion of the local area characteristics and the edge characteristics of the image is realized, the blocking effect phenomenon of the conventional multiresolution MRF model in an optimization process is solved, and the edge of an image segmentation resultis effectively kept.
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Description

technical field

[0001] The invention belongs to the technical field of image segmentation, and in particular relates to an image segmentation method of a multi-scale MRF model with an edge preserving function. Background technique

[0002] Image processing methods based on multi-scale MRF (Markov Random Field) models have been widely used. This multi-scale MRF structure often adopts the multi-resolution method of the image, using the lower-resolution image to describe the global features of the image, and the higher-resolution image to describe the detailed features of the image, and then through the causality between the layers of the multi-scale MRF model Relationship, build a top-down image segmentation algorithm.

[0003] The image segmentation algorithm based on the multi-scale MRF model has low computational complexity, but the quadtree structure of the commonly used multi-scale MRF model leads to blurring or even loss of edge details in low-resolution images during t...

Examples

Embodiment Construction

[0066] The present invention will be further described in detail below in conjunction with the accompanying drawings, so that those skilled in the art can implement it with reference to the description.

[0067] see figure 1 Shown, a kind of multi-scale MRF model image segmentation method with edge preservation of the present invention, comprises the following steps:

[0068] Step 1: Input a natural image to be segmented.

[0069] Step 2: Parameter initialization: Determine the number of segmentation categories K, the number of multi-scale layers L, and the initial value of the edge scale factor η.

[0070] 2a) Let Ω={1,2,...,K} represent the pixel node label space, and manually determine the number K of segmentation categories.

[0071] 2b) Given the number of layers L of the multi-scale MRF model, according to the experimental results and the operational complexity requirements of the RBP algorithm, it is more appropriate to choose the number of layers of the EPMRMRF model...