Supervision-free Markov random field image segmentation method

An image segmentation and random field technology, which is applied in image enhancement, image data processing, instruments, etc., can solve the problems of not fully considering pixel gray level difference and positional relationship, etc., so as to reduce misclassification, improve anti-noise performance, and misclassify Effect of Classification Rate Reduction

Inactive Publication Date: 2008-10-15
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0016] First, the definition of the potential energy function in formula (4) does not fully consider the gray level difference and positional relationship between pixels in the neighborhood subcluster

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  • Supervision-free Markov random field image segmentation method
  • Supervision-free Markov random field image segmentation method
  • Supervision-free Markov random field image segmentation method

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Embodiment Construction

[0033] The present invention will be further described now in conjunction with accompanying drawing:

[0034] The present invention uses three images as an implementation example, including an artificially synthesized noise image, a standard image with noise added, and a real blood cell image. The result is shown in Figure 2. The experimental steps are as follows:

[0035] 1) First, for the original images A, B, and C, as shown in the first column in Figure 2, determine that the range of possible classification numbers for each image is 1 to 9;

[0036] 2) For A, B, and C, the K-means method is used to segment 1 to 9 categories, and each image obtains 9 initial segmentation results;

[0037] 3) For each of the 9 segmentation results of A, B, and C, calculate the BIC value under each segmentation result, and obtain the BIC value of each image { BIC m min , BIC ...

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Abstract

The invention relates to an unsupervised Markov random field image segmentation method, which is technically characterized in that: the scope of the classification number of an image M is firstly determined, the image M is then carried out the K-means segmentation from m min to m max type, the BICm which is corresponding to the image M under each type is calculated according to the BIC criteria, a potential energy function is applied to calculate the total energy of the image M, and finally the ICM method is selected to complete the Markov random field image segmentation. The method has the advantages that: the new potential energy function is applied to image segmentation, the anti-noise performance thereof is obviously improved, the segmentation result can remove most of the noise of the image with great noise, the error classification phenomenon can be simultaneously effectively reduced, the error classification rate is reduced by more than 60 percent; the classification number does not need to be manually determined, thus basically realizing the unsupervised image segmentation; and the judgment accuracy of the classification number can achieve more than 95 percent by the artificial synthetic image test.

Description

technical field [0001] The invention relates to an unsupervised Markov random field image segmentation method, belonging to image segmentation methods. Background technique [0002] Image segmentation can be viewed as an image labeling problem. Let S={s=(i, j)|1≤i≤W, 1≤j≤H} be a two-dimensional grid point set defined on an image, where W and H are the width and height of the image . tag field x = {x 1 ,...X m}m=W×H is the random field corresponding to the two-dimensional grid point set S. X={x can be used 0，1，m ∈L} represents a possible labeling situation of an image, where L={1,2,...,M}, M is the total number of types of classification labels. [0003] The posterior probability of the segmentation result can be expressed as: [0004] P ( X | Y ) = P ( Y | X ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 郭雷侯一民
Owner NORTHWESTERN POLYTECHNICAL UNIV
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