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.
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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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