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Image segmentation active contour method based on global and local information self-adaptive adjustment

A technology of self-adaptive adjustment and local information, applied in the field of image processing, can solve the problem of inability to segment heterogeneous images with gray distribution, and achieve the effect of high speed, avoiding complex calculation, and improving the speed of evolution.

Inactive Publication Date: 2016-05-04
LIAONING NORMAL UNIVERSITY
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Problems solved by technology

However, the model still fails to solve the problem that the GAC and C-V models cannot segment heterogeneous images with uneven gray distribution

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  • Image segmentation active contour method based on global and local information self-adaptive adjustment
  • Image segmentation active contour method based on global and local information self-adaptive adjustment
  • Image segmentation active contour method based on global and local information self-adaptive adjustment

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Embodiment Construction

[0031] An active contour method for image segmentation based on global and local information adaptive adjustment, characterized in that: energy functional function is defined The form is as follows,

[0032]

[0033] in, The item is a global information item, which makes the model evolve rapidly in the uniform gray area; The item is a local information item, which makes the model evolve more delicately in the area with detailed information; The term is a penalty term to correct the error of the level set function; is about images The adaptive balance function of is the image gradient modulus, is a constant, which is used to adaptively balance the global information and local information of the image; is a positive parameter, and its value range is ; and are two constants, which are approximately equal to the average gray value inside and outside the contour respectively, respectively in A smooth function of the local gray value of the image near the...

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Abstract

The invention provides an image segmentation active contour method based on global and local information self-adaptive adjustment. The method comprises the following steps of (1) defining a novel self-adaptive balance function, wherein the weight of each part can be automatically regulated by using the novel self-adaptive balance function according to the self characteristics of an image, and further the curve evolution is driven; (2) in a weight function, adding a Gaussian filter process for regularizing a level set function, and meanwhile, adding a decreasing factor for accelerating the curve evolution speed; and (3) ensuring the precise calculation and the stable evolution of a model through introducing a penalty term. The method has the advantages that good segmentation effects are achieved on the segmentation precision and the processing speed; and the segmentation on a heterogeneous image with nonuniform gray level distribution can be realized.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image adaptively adjusted based on global and local information, which has fast calculation speed, is robust to the position of the initial contour curve and image noise, and can segment heterogeneous images with uneven gray distribution. Segmentation active contour method. Background technique [0002] At present, image segmentation has been widely used in daily life, medical technology, military and other fields, such as target extraction, edge detection, target tracking and so on. Image segmentation based on the active contour model is an important theoretical method innovation in this field. Its basic idea is to use the geometric characteristics of the image to establish an energy functional, and to find the minimum value of the energy function under the variational method to obtain the corresponding Euler- Lagrange equation, and then use the relevant knowledge in the fiel...

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Application Information

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IPC IPC(8): G06T7/00G06T7/40
CPCG06T2207/10081G06T2207/10088G06T2207/20024G06T2207/30016G06T2207/30101
Inventor 王相海方玲玲张冲宋若曦
Owner LIAONING NORMAL UNIVERSITY
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