A Robust Global Threshold Segmentation Method
A global threshold and robust technology, applied in the field of image processing, can solve problems such as unsatisfactory results and uneven cumulative probability distribution, and achieve the effect of good versatility and strong robustness.
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Embodiment example 1
[0047] Such as figure 2 , Shows an image with a large difference in gray distribution between the foreground and the background and the corresponding histogram, T o With T b And Ts are 126, 142, 85, λ=0.127, which is greater than th. It shows that the Otsu threshold will be biased towards the low gray level, which is the background in this image. Therefore, the optimal criterion adopted is p 1 (m1mg) 2 , At this time, it shows that the variance of the high-gray-level image compared with the low-gray-level image has no effect 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), 3(d) respectively indicate that the threshold is equal to T o , T b , T i The segmentation results and manual segmentation results. The result shows that the present invention successfully segmented the foreground object, but the Otsu threshold method failed.
Embodiment example 2
[0049] Such as Figure 4 (a) shows an image with poor contrast and far greater foreground variance than background variance. Figure 4 (b) is the result after the histogram equalization. It can be seen that the contour of the foreground is not obvious before the histogram equalization. This is mainly because the gray distribution range of the foreground is too wide. This can be seen from the histogram. . T o With T b They are 89 and 108, and Tf is 29. Figure 5 (a) and 5(b) respectively show the Otsu threshold T o And the threshold T obtained by the algorithm of the present invention f The segmentation result. The comparison shows that the present invention correctly separates the object and the background, but the Otsu algorithm fails, which again verifies the effect of the present algorithm on images with significant differences in the front background variance.
Embodiment example 3
[0051] Such as Image 6 (a) and 7(a) are the material images and text images segmented by Otsu's threshold method. The extracted objects are too small and too large, and Image 6 (b) and 7(b) are the segmentation results of the present invention, and the images are correctly separated. The segmentation of the two images also shows two application prospects of the present invention, which are binary image processing with uneven illumination and material image processing.
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