Method for optimizing multilayer image segmentation of multiclass color texture images based on variation model

A texture image and variational model technology, applied in the field of image processing, can solve problems such as unstable feature distribution description, difficulty in describing image nonlinearity and continuous feature changes, and prone to false target areas

Inactive Publication Date: 2014-10-08
HUANGHE S & T COLLEGE
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However, when region-based methods segment complex and varied natural images, the final segmentation results are prone to false target regions, and the captured target edges are not smooth enough. If the edge-based method is combined with the region-based method, it can Get better segmentation results
However, the area corresponding to each calibration color uses...

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  • Method for optimizing multilayer image segmentation of multiclass color texture images based on variation model
  • Method for optimizing multilayer image segmentation of multiclass color texture images based on variation model
  • Method for optimizing multilayer image segmentation of multiclass color texture images based on variation model

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[0087] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0088]The present invention provides a multi-layer graph cut optimization segmentation method for multi-category color texture images based on a variational model, which is specifically implemented according to the following steps:

[0089] Step 1. Establish a multi-category continuous variational active contour model, and obtain the energy function of the multi-category variable active contour model; specifically implement according to the following steps:

[0090] Step 1.1. For a color texture image, denote it as u 0 , and its corresponding image domain is denoted as: Ω: R 2 → R, Ω corresponds to the segmentation region boundary subset is C, it will color texture image u 0 Divide into several disconnected sub-regions Ω j , and satisfy: and Where N(Ω) represents the number of all disconnected segmentation regions, j represents the sub...

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Abstract

The invention discloses a method for optimizing double-layer image segmentation of multiclass color texture images based on a variation model. The method comprises the steps of establishing a multiclass variation active contour model, obtaining an energy function of the multiclass variation active contour model, carrying out disperse expression on the energy function of the multiclass variation active contour model, establishing a multilayer image segmentation model, solving the energy function of the multiclass variation active contour model after disperse expression to obtain a globally near optimal solution, and carrying out multi-layer image segmentation minimality optimization on multiclass disperse variation active contour energy in an iteration mode to achieve stable segmentation.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a multi-class color texture image segmentation method based on a variational active contour model. Background technique [0002] At present, the image segmentation method based on the variational model has received extensive attention. It is widely used in visual tracking and target detection because it can provide smooth and closed curves and can combine prior information to obtain the desired non-homogeneous target boundaries. , scene understanding, industrial inspection, content-based image retrieval, and medical image analysis. Since the image contains multiple non-homogeneous target areas, although many scholars have proposed multi-class segmentation methods, they all use constant density descriptions and use interactive methods to mark target areas or sub-target areas, resulting in segmentation The results of the method depend largely on prior informat...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 杨勇郭玲周小佳胡爱娜王缓缓武海燕
Owner HUANGHE S & T COLLEGE
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