A method and device for image edge detection based on genetic algorithm

Abstract

The invention provides an image edge detection method and device based on a genetic algorithm. The method includes: S100 generating an initial population; S200 performing calculations on each image edge point to obtain fitness values ​​of each image edge point; S300 judging whether the fitness value reaches the average Fitness value; if so, execute S400; S400 selects the image edge points corresponding to the average fitness value; S500 adjusts the crossover probability of each selected image edge point; Perform cross mutation operation on the edge points to obtain the next generation population; S700 judges whether the next generation population meets the preset termination condition; if so, execute S800; otherwise, return to S200; S800 obtains the edge points of the target image, according to the gray value of the edge points of the target image Perform image edge detection. The purpose of the present invention is to obtain better edge detection effect and improve calculation efficiency.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image edge detection method and device based on a genetic algorithm. Background technique [0002] With the rapid development of computer technology, people are increasingly using computers to help humans acquire and process visual image information. According to statistics, 80% of the information that humans obtain from the outside world comes from vision, including images, graphics, videos, etc. It is the most effective way for people to obtain and communicate information. , The performance is intuitive and occupies a very important position in the multimedia processing technology. [0003] The most basic feature of an image is the edge. The so-called edge refers to the collection of pixels whose gray levels around them have a step change or a roof change. It exists between the target and the background, the target and the target, and the region and the region. It is not on...

AI Technical Summary

Problems solved by technology

[0004] In the prior art, the edge detection technology based on the gray gradient operator needs to determine the gray threshold of the image based on prior knowledge. However, the prior knowledge is often summed up based on past experience and cannot be well adapted to different situations. In this case, the processing results largely depend on the preset gray threshold; in addition, the implementation of this technology is relatively complicated in the actual application process, and the amount of calculation is large, so the edge detection technology based on the gray gradient operator is adaptive and Efficiency is often unsatisfactory

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