Abstract
Image segmentation is an important part of image processing. The result of image segmentation directly affects the effect of subsequent image processing. However the efficiency of the traditional maximum class variance method is low. This paper uses the cuckoo algorithm to optimize the traditional maximum class variance method to achieve a better segmentation effect. This image segmentation method combined with optimization theory can achieve the purpose of finding the optimal segmentation.
Highlights
With the rapid development of machine vision and other fields in recent years, the technology of digital image processing is becoming more and more important
Image processing technology is used in medical image field, military image field, agriculture and industry field and so on
Image segmentation is the basis of subsequent image processing technology such as image recognition
Summary
With the rapid development of machine vision and other fields in recent years, the technology of digital image processing is becoming more and more important. Image processing technology is used in medical image field, military image field, agriculture and industry field and so on. Image segmentation is the basis of subsequent image processing technology such as image recognition. The quality of image segmentation results directly affects the subsequent processing results. Image segmentation technology is to divide the image into non-overlapping parts according to different characteristics. Because the traditional maximum inter-class variance method is essentially exhaustive. The inter-class variance corresponding to all gray values needs to be calculated. We hope to use cuckoo optimization algorithm to optimize the optimization process of traditional inter-class variance method
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