Robust global threshold segmentation method

A global threshold and threshold method technology, applied in the field of image processing, can solve the problems of uneven cumulative probability distribution and unsatisfactory results, and achieve the effect of good versatility and strong robustness.

Active Publication Date: 2015-06-24
INST OF SEMICONDUCTORS - CHINESE ACAD OF SCI
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when the area, variance, and cumulative probability distribution of the image foreground

Method used

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Examples

Experimental program
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Effect test

Embodiment example 1

[0047] Such as figure 2 , showing an image with a large difference in the gray distribution of the foreground and background and the corresponding histogram, T o with T b And Ts are 126, 142, 85 respectively, λ=0.127, greater than th. It shows that the Otsu threshold will be biased towards a class of low gray levels, which is the background in this image. Therefore, the optimal criterion adopted is p 1 (m1mg) 2 , which indicates that the variance of images with high gray levels compared to images with low gray levels does not play a role in determining the optimal threshold. After adopting the optimal criterion, the obtained threshold is equal to 85, that is, the optimal threshold is obtained, T f =T i . image 3 (a), 3(b), 3(c), and 3(d) respectively indicate that the threshold is equal to T o , T b , T i segmentation results and manual segmentation results. The results show that the invention successfully segmented the foreground object, while the Otsu threshold ...

Embodiment example 2

[0049] Such as Figure 4 (a), shows an image with poor contrast and the foreground variance is much larger than the background variance. Figure 4 (b) is the result after histogram equalization. It can be seen that before the histogram equalization, the outline of the foreground is not very obvious. This is mainly because the gray distribution range of the foreground is too wide, which can be seen from the histogram . T o with T b are 89 and 108 respectively, and Tf is 29. Figure 5 (a), 5(b) respectively show the Otsu threshold T o And the threshold T obtained by the algorithm of the present invention f segmentation results. The comparison shows that the present invention correctly separates the object and the background, while the Otsu algorithm fails, which again verifies the effect of the algorithm on images with significant differences in variance between the foreground and the background.

Embodiment example 3

[0051] Such as Image 6 (a), 7(a) are material images and text images segmented by Otsu threshold method, the extracted objects are smaller and larger respectively, while Image 6 (b) and 7(b) are the segmentation results of the present invention, and the images are correctly separated. The segmentation of these two images also demonstrates two application prospects of the present invention, which are binary image processing and material image processing with uneven illumination.

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Abstract

The invention provides a robust global threshold segmentation method. The method includes the steps that step1, a threshold value To of a gray-level image is calculated through an Otsu threshold value method; step2, a balance degree factor eta is defined through the variance of a foreground and a background of the gray-level image, and a threshold value Tb with the balance degree factor as the criterion is acquired; step3, a deviation degree lambda and a new optimal criterion zeta are defined according to Tb and To, and a segmentation result is generated. By means of the method, the optimal threshold value can be obtained under the condition that the probability distribution difference between the foreground and the background of the grey-level image is obvious or under general conditions.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a robust global threshold segmentation method, which can avoid the failure of the most widely used Otsu threshold method when the difference in the gray variance of the background image before segmentation is very large, and can also be used in In general, it achieves the same result as the Otsu method, and it is a new threshold scheme that is better than the Otsu threshold method. Background technique [0002] The current image segmentation methods can be divided into methods based on pixels or regions. For example, the threshold method obtains the gray threshold to binarize the image, and the region growing method obtains the segmentation contour through the initial seed point and the growth criterion based on the gray information, etc. ; Boundary-based methods, calculate strong edges through the edges of various gradient operators such as sobel, and then conne...

Claims

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Application Information

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IPC IPC(8): G06T7/00
Inventor 龙鹏鲁华祥边昳徐露露王俭陈旭龚国良金敏陈刚
Owner INST OF SEMICONDUCTORS - CHINESE ACAD OF SCI
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